Seismic Vulnerability Index Assessment Framework of RC Structures (SpringerBriefs in Applied Sciences and Technology) 9819950376, 9789819950379

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SpringerBriefs in Applied Sciences and Technology Moustafa Moufid Kassem · Fadzli Mohamed Nazri

Seismic Vulnerability Index Assessment Framework of RC Structures

SpringerBriefs in Applied Sciences and Technology

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Moustafa Moufid Kassem · Fadzli Mohamed Nazri

Seismic Vulnerability Index Assessment Framework of RC Structures

Moustafa Moufid Kassem School of Civil Engineering Universiti Sains Malaysia Nibong Tebal, Malaysia

Fadzli Mohamed Nazri School of Civil Engineering Universiti Sains Malaysia Nibong Tebal, Malaysia

ISSN 2191-530X ISSN 2191-5318 (electronic) SpringerBriefs in Applied Sciences and Technology ISBN 978-981-99-5037-9 ISBN 978-981-99-5038-6 (eBook) https://doi.org/10.1007/978-981-99-5038-6 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

This book represents a significant step toward a new contribution in the process of developing the seismic vulnerability index. This is accomplished by releasing or reducing the role of the rapid visual screening that is created by the opinions and decisions of experts, which depend on observations made while investigating the vulnerability damages caused by earthquakes. Alternatively, the computational analytical technique is preferable since it can be effective in determining the seismic vulnerability index before to the occurrence of an earthquake. This is accomplished by modeling the most affected influencing parameters that regulate the building performance. In addition, the seismic vulnerability index is supported by the vulnerability curves, which describe the probability of damages and are used to estimate the economic damage grade for each building that is the topic of inquiry. In the end, this can help to establish a clear vision and sort of recommendations for engineers and specialists to follow in order to take into consideration certain indices and factors before designing any specific structure. Because of this, the simplified work may be utilized to manage and put into action measures that will protect against the effects of seismic events before an earthquake really occurs. In addition to this benefit, the work that has been done might be of significant assistance to the authorities that are accountable for the restoration of the preexisting buildings and the cultural heritages. In Chap. 1, the main goal and the problem that this framework is supposed to solve are explained. This was done by making a better version of the original vulnerability index method, which uses non-linear parametric analysis to figure out which parts of the physical structure are the most useful overall. The analytical framework is quickly becoming the new favorite way to model because it takes into account the standard precautions that need to be matched with the availability of damage that can be seen. To do this, designers can model and build parameters that have a big impact on how the structure works, and they can also make a framework by coming up with an analytical Seismic Vulnerability Index. In Chap. 2, a proposed method using a seismic vulnerability index (SVI) is presented for evaluating the earthquake risk of reinforced concrete (RC) structures. The technique employs a hybrid of the European Macroseismic and the Italian GNDT methods. Eight parameters spanning three distinct vulnerability classes were modeled v

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for the estimation of RC vulnerability indices. Structures were ranked from least to most earthquake-prone based on their ERD. The importance of each parameter in calculating the seismic vulnerability index for a particular Peak Ground Acceleration was determined through Non-Linear Time History Analysis and Non-Linear Static Analysis. Recognize that earthquake preparedness can be rated on a scale from 0 to 1. After the SVI has been determined, the average damage grade can be constructed. Chapter 3 verifies the validity of the framework; this chapter gives the study’s findings and discussion for the two Malaysian buildings that were chosen for the study. The following components are necessary for validating the SVI scorers: (1) defining a standardized three-stage procedure for conducting SVI; (2) enhancing the vulnerability index by quantifying the impact of the most vulnerable parameters (P1–P8) on seismic behavior development; (3) developing an SVI based on dynamic and static analysis; (4) developing a mean damage grade, vulnerability curve, and fragility curve using a probabilistic approach; and (5) finally validating the results based on plastic hinges formation and mean damage states classifications. Chapter 4 represents the final conclusion and future recommendations for potential improvements on the current work. Nibong Tebal, Malaysia

Asst. Prof. Moustafa Moufid Kassem, Ph.D. Assoc. Prof. Fadzli Mohamed Nazri, Ph.D.

Acknowledgement This research work is financially supported by Universiti Sains Malaysia, under the Research University Individual (RUI) Grant Scheme (8014080).

Contents

1 Contribution of Vulnerability Index in Earthquake Assessment . . . . . . 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 To What Extent and in What Situations Does a Seismic Design Need to be Implemented? . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.2 Is It Better to Use an Empirical or Analytical Method? . . . . . 5 1.2 Indication of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.1 Framework Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Seismic Vulnerability Index Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 The New Seismic Vulnerability Index (SVI) Approach . . . . . 2.2 Strategy Adopted for Selecting the Modeling Parameters . . . . . . . . . . 2.2.1 Beam-Column Joint Connection (P1) . . . . . . . . . . . . . . . . . . . . 2.2.2 Boundary Conditions (P2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Horizontal Diaphragm System (P3) . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Type of Soil (P4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.5 Ductility Level (P5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.6 Horizontal and Vertical Mass Irregularity (P6 and P7) . . . . . . 2.2.7 Material Strength (P8) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Ground Motions Records . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Earthquake Resistant Design Concept-Based Vulnerability Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Analytical Method for an Improved Seismic Vulnerability Index . . . 2.5.1 Incremental Dynamic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Vulnerability Classifications of RC-Buildings . . . . . . . . . . . . . 2.6 Probabilistic Damage Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.1 Mean Damage State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.2 Fragility Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15 15 15 16 18 19 20 21 23 24 25 26 26 27 27 30 31 31 33

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2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3 SVI Framework Application and Validation . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Unified Seismic Vulnerability Index Assessment Framework . . . . . . . 3.3 Validation of the Selected Analytical Models During 2015 Ranau Damages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Observational Damage Features . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Validating the Findings from the Analytical Models . . . . . . . . 3.4 Displaying the Visual Output in a Map . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Seismic Vulnerability Index Mapping . . . . . . . . . . . . . . . . . . . . 3.4.2 Direct Physical Damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

39 39 40

4 Conclusions and Recommendations for Potential Research . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Based on the Results of the Analysis, the Following Conclusions Were Drawn: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

79 79 80

48 48 50 55 55 60 72 76

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Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

About the Authors

Dr. Moustafa Moufid Kassem is currently working as Assistant Professor at School of Civil Engineering of Universiti Sains Malaysia. His research focuses on the structural vulnerability of RC buildings and urban areas exposed to natural hazards, specifically earthquakes. More recently, he has expanded his focus to include the seismic vulnerability interaction between the structures and infrastructures in the context of physical vulnerability. In addition, his research includes the use of probabilistic and statistical tools for modeling of extreme loads on structures related to risk assessment, seismic hazard, structural analysis, numerical simulation, and modeling. He is recognized as among professionals’ engineers with Associate Membership qualification from American Society of Civil Engineers (ASCE) and Associate Membership qualification from Institution of Civil Engineering (ICE). He was the first who developed a new standard seismic design code for bridges in Malaysia to be updated in Malaysia National Annex. He has published three books and more than 40 reputable ISI/Scopus research papers in peer-reviewed journals in the field of structural and earthquake engineering, and eight copyrights in Malaysia.

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About the Authors

Assoc. Prof. Dr. Fadzli Mohamed Nazri is an Associate Professor with research interest on risk assessment, seismic hazard, structural analysis, numerical stimulation and modelling. On top of this, his research has given him a deeper understanding and desire to learn more on earthquake engineering. Currently, he is an Associate Professor of Structural and Earthquake Engineering in the School of Civil Engineering at Universiti Sains Malaysia (USM). He gained his undergraduate degree and M.Sc. by Research in Civil Engineering at Universiti Sains Malaysia (USM). He completed his Ph.D. in structural and earthquake engineering from University of Bristol, UK in 2012. His Ph.D. research project during the Ph.D. was on predicting the collapse of the buildings by using numerical analysis. He has published 6 books, more than 50 reputable ISI/scopus/ era research papers in the field of structural and earthquake engineering, 5 book chapters and 5 policy papers with the Department of Standard Malaysia. He is the member of several professional bodies at national and international level. He is an active member of International Association of Engineers, American Society of Civil Engineering and exclude many more.

Chapter 1

Contribution of Vulnerability Index in Earthquake Assessment

1.1 Introduction Seismic risk management in many countries around the world is a subject of crucial interest to today’s societies as the destructive effects of recent powerful earthquakes cannot be overlooked or ignored. Some of these seismic disasters that have hit Sabah (Malaysia) in 2015, Gorkha (Nepal) in 2015, Northwest Ecuador in 2016, Amatrice (Italy) in 2016, and Chaiapas (Mexico) in 2018 have affected millions of people and caused severe economic losses (Parra et al. 2016; Adhikari et al. 2015; RamirezHerrera et al. 2018; Faenza et al. 2016). Accordingly, seismic design codes and frameworks are typically applied or improved after each earthquake disaster, but older buildings have not been updated to take advantage of these new methods, and they have shown poor behavior when subjected to the same seismic standards as newer constructions. Seismic risk has become the greatest challenge for both, global economic development and population security, as reported by the United Nations Development Programme (UNDP). Such events highlight the need to improve awareness about the vulnerabilities of our cities, but despite the considerable attention and debate in the research and scientific communities, the development of real risk policies based on a proper understanding of these vulnerabilities is still very limited and restricted. Risk management in many areas is often implemented without the use of a practical general planning mechanism that restricts the ability of technicians and decisionmakers to evaluate a particular field under investigation, which potentially affects the success of their risk mitigation measures. In this case, current seismic mitigation methods applied to reduce losses require tremendous effort, especially the empirical methods that require sufficient data for development and implementation. This has been recently recognized as a risk management methodology, a process that determines mitigation efforts in the case a seismic hazard, identifying these seismic hazards as well as the scale of management and its priorities.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. M. Kassem and F. Mohamed Nazri, Seismic Vulnerability Index Assessment Framework of RC Structures, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-981-99-5038-6_1

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Thus, one of the most important challenges in the field of risk analysis is how to define a new advanced analysis method that takes into account the interdependencies between different parameters, particularly for structural buildings, that depend on the design and construction characteristics (structural configuration, geometric form, ductility level, material, structural element connections, and others), therefore, constituting an independent factor from the seismic actions. In addition, the evaluation of seismic risk management is considered important for determining urgent strategic planning and management initiatives that should focus on structural analysis, the exposed population, and its emergency interaction. In order to realize this task, it is important to reliably classify the most vulnerable areas/zones as well as to plan and execute tactical and field exercises aimed at modulating practical emergency scenarios through civil protection bodies, the government, and pertinent agencies. The recent moderate earthquake that hit East Malaysia has triggered fresh concerns about the preparedness of the Malaysian government to deal with seismic tremors. The Ranau, Sabah, quake of June 2015, measured 5.90 on the Richter scale and was the strongest earthquake to hit Malaysia since 1976 (Malaysian Meteorological Department or MMD). This earthquake was due to the sliding contact between the Philippines and Australian tectonic plates (Manafizad et al. 2016). Sabah has experienced an earthquake with a maximum intensity of level VIII on the Modified Mercalli (MM) scale, making it the most tectonically active region in East Malaysia. Many facilities were damaged by the earthquake due to the lack of awareness regarding the incorporation of seismic design regulations into the structural design. Therefore, the seismic resistance concept pertaining to Malaysian buildings is still unclear although recorded data show a highly concentrated distribution of earthquakes around this region (Megawati et al. 2003; Pan and Megawati 2002). However, when earthquake distribution is very scarce, the seismic hazard is generally assumed to be negligible. This interpretation is problematic because a number of small to medium magnitude earthquakes have occurred in this region, which requires a thorough examination of existing structures that use a standard seismic vulnerability assessment framework, such as the Seismic Vulnerability Index (SVI). The current framework layout in this study is intended to facilitate decisionmaking prior to the occurrence of an earthquake (pre-earthquake). To accomplish this, vulnerability modeled parameters are developed for each of the selected reinforced concrete (RC) buildings based on the SVI. These parameters include beam-column joint connection, support boundary condition, horizontal diaphragm system, type of soil, ductility level, horizontal and vertical mass irregularity, and material strength. This framework is a component of the Global Earthquake Model (GEM), which was developed and empirically validated through post-earthquake investigations of damaged RC-buildings in Malaysia. These investigations were carried out in Malaysia. The framework that has been proposed is founded on an analytical method that quantifies the influence that the physical performance of a parameter has on the structural behavior that is determined by the concept of Earthquake Resistant Design (ERD). The results of the SVI and the vulnerability function, both of which are related

1.1 Introduction

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to the probability of losses and can be integrated uniformly into risk-based cities, are unaffected by the seismicity of the region that was investigated. It is beneficial to implement public protection strategies with the objective of predicting mean damage states in a manner that is consistent with performance-based design criteria (Roohi and Hernandez 2020; Mendes and Lourenco 2015; Anagnostopoulos and Moretti 2008a, b). Indeed, developing the SVI framework for RC-buildings in Malaysia is important for a number of reasons, some of which are mentioned below and could serve as a model for other countries around the world. i. Several countries and building classes have not been evaluated using any vulnerability assessment approach. ii. Varied vulnerability assessment methods can produce quite different risk consequences. Thus, SVI development will avoid bias in risk assessment across areas or countries. iii. Unlike masonry structures, RC-building vulnerability models and SVI are not studied.

1.1.1 To What Extent and in What Situations Does a Seismic Design Need to be Implemented? Recent earthquakes have emphasized the need for earthquake engineering studies to evaluate the susceptibility of older buildings without sufficient seismic resistant attributes (Rizzano and Tolone 2009). In fact, earthquakes are more likely in cities that build without considering seismic forces. Because of this, scientists are always developing more detailed frameworks to evaluate current structures’ seismic performance and capacity (Hassani et al. 2020; D’Ayala et al. 2020; Yon et al. 2020; Booth 2018; Zhang et al. 2017; Akadiri et al. 2012; Ciurean et al. 2013; Bruneau et al. 2003). The purpose of the SVI and standardized risk evaluation principles is to provide a framework that can be used everywhere, regardless of the differences in building construction or seismicity. ERD is a term used to describe the practice of constructing buildings in accordance with established guidelines for reducing the risk of damage caused by earthquakes (Fathi-Fazl et al. 2020). It goes without saying that stricter design guidelines result in greater resistance to seismic forces and less susceptibility (Lai et al. 2012). Due to this, in late 2017, Malaysia adopted the Malaysian National Annex (NA) to Eurocode 8 (EC8), making it the first national code for seismic design of structures in the country (Malaysia NA 2017; EC8 2004). Before the implementation of seismic design rules, the local building sector largely followed the principles established by British Standards (BS). After numerous investigations, scientists have attempted to quantify the intensity measure of Peak Ground Acceleration (PGA) for design purposes (Harith et al. 2019; Loi et al. 2018; Looi et al. 2017; Majid et al. 2017; Lam et al. 2016; Hee 2014; Pappin

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Fig. 1.1 Seismic zonation map featuring the minimum design of PGA according to Malaysian NA 2017

et al. 2011; Sooria et al. 2012; Petersen et al. 2008; Adnan and Harith, 2017, USGS 2008). A seismic zonation map with a 10% probability of exceeding in 50 years was created by the Malaysian NA using data from a Probabilistic Seismic Hazard Analysis (PSHA). When trying to determine where earthquakes come from, this is one of the biggest challenges. Therefore, as illustrated in Fig. 1.1, structures should be constructed to withstand the acceleration and frequency specified by the code. In regions with low to moderate seismicity, practicing engineers have a limited understanding of how to include ductility into the structural design process. When it comes to authorizing and mandating the use of the Ductility Class Medium (DCM), this is the primary challenge. If the risk level is greater than the threshold value of 0.09 g, the EC8 specification mandates that the ductility feature must be adopted in an obligatory manner. In some locations throughout Malaysia, the threshold value of 0.09 g has been attained; however, DCM is not yet required and is acceptable only for regions with strong seismicity. According to the PGA, two locations in Sabah, namely Ranau and Lahad Datu, should presume about implementing this seismic design because the intensity significantly exceeds 0.09 g, which is equivalent to 9% of gravity. Additionally, the intensity is marginally above 0.09 g in north Sarawak (Niah) and in the south of Peninsular Malaysia. This indicates that Malaysia, particularly the eastern portion of the country, should adopt this framework in order to provide a safe and sustainable built environment that generates a desirable building performance when subjected to seismic pressures. Even though there is no information on the vulnerability of a specific building in a region, it is possible that the building attributes and construction techniques were

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comparable due to the absence of seismic rules during the period in which the building was constructed. This demonstrates the benefit of using the SVI assessment methodology, which makes it possible to recognize relativities in the vulnerability of the countries that are being represented. Validation processes are helped along by these relativities in nations like Malaysia that do not have access to past validation data. Therefore, in the proposed framework, the impact of seismic design is measured by adjusting and distributing the vulnerability classes into three levels of ERD, namely the Low-ERD, Moderate-ERD, and High-ERD, which can develop the SVI and mean damage grades for any existing RC-building in Malaysia. These three levels of ERD are as follows: Low-ERD, Moderate-ERD, and High-ERD.

1.1.2 Is It Better to Use an Empirical or Analytical Method? In general, evaluations of seismic loss and damage are based either on the traditional empirical (or statistical) technique or the more contemporary analytical approach (Jiménez et al. 2018). For the purpose of determining the extent of earthquake damage in Malaysia, the analytical method is recommended for use in situations in which the empirical approach cannot be utilized as a result of a lack of relevant data or an absence of prior experience with assessing the effects of seismic activity. The empirical vulnerability index evaluation method, for instance, relies on postearthquake inspections and visual observations of buildings to analyze the fundamental structural system and the underlying seismic-related concerns derived through field damage assessments. The National Group of Défense (GNDT) earthquake defense strategy categorizes these deficiencies by several factors (Terremoti 1993). The quantification of each parameter is dependent on the judgment of the evaluator, which may not demand a high level of skill but does require a fundamental knowledge and awareness of the structural principles. In addition to this, there is a lack of consistency and variety in the weighting of the parameter when classifying the vulnerability of a certain structure. This is something that has been mentioned in a lot of research papers. The empirical technique is used to provide statistics and precise information regarding building vulnerability; however, it relies on expert opinion and assumptions that are represented negatively in the questions below. i. How to guarantee the accuracy of the inventory data? ii. How are the data pertaining to damages to be interpreted? iii. How to weigh a crucial analysis parameter that is occasionally unavailable (e.g., reinforcement details, soil type, etc.)? iv. How to give a building a vulnerability class? v. In the European Macroseismic method, building typologies are characterized into susceptibility classes based on material and structural system (Grunthal

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1998; Milutinovic and Trendafiloski 2003). What are the other factors (beamcolumn joint connections, irregularity, mass distribution, etc.) that directly affect the physical damages to a building? In addition, determining the amount of damage sustained by older buildings that are still standing can be a difficult task. This is not only due to the fact that the building was not constructed in accordance with earthquake-resistant specifications, but also due to the fact that the quality of the materials that were used is doubtful and uncertain. Because of this, it is possible to reach the conclusion that the empirical method cannot be utilized because it is predicated mostly on the information that is accessible, which is either very limited or non-existent in a nation such as Malaysia. This work needs to be carried out in accordance with the customs and practices of building, as well as the quality of the building materials that are readily available in the area. The objective of this method is to produce numerical models of various types of buildings. On the other hand, the analytical framework is the preferred method for modeling since it considers the standard precautions that must be matched with the availability of observable damage features. To do this, designers can model and establish parameters that substantially affect the structure’s behavior, as well as create a framework by creating an analytical SVI to use as a grading signal for categorizing vulnerability classes. In other words, this can be accomplished by analyzing and modeling earthquakes.

1.2 Indication of the Problem In order to aid in emergency earthquake management and protection strategy planning, seismic risk analysis should be addressed. This needs vulnerability and damage evaluation, which can be undertaken on a large or regional scale, particularly in building structures. The incorporation of seismic rehabilitation guidelines in order to make it possible for specialists and engineers to design seismic provisions is one of the main goals of risk reduction, which is considered to be one of the primary earthquake risk management strategies. The goal of risk reduction is to lower the seismic vulnerability of structures that are affected by an earthquake (Galanis et al. 2018). The Sendai Framework for Disaster Risk Reduction 2015–2030, which was adopted by the United Nations (UN), is an example that highlights the need for increasing the resilience of cities and reducing disasters in communities. This framework, which complements the Hyogo Framework of 2005–2015, is an example that highlights the need for increasing the resilience of cities and reducing disasters in communities (UNISDR 2015; ISDR-UN 2005). These two framework documents shed light on the requirement of information about cities to strengthen their safety and resilience in terms of lives as well as social and economic assets. This knowledge may be gained through research and observation of cities.

1.2 Indication of the Problem

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On the one hand, this is because of disparities in the ways in which data are acquired and processed, and on the other hand, this is because of discrepancies in the descriptions of damage, which are often based on the judgment of experts. Because of the inherent unreliability of the methodologies that are currently being used, it is impossible to reliably classify the parameters that have the greatest impact on the amount of physical damage a structure sustains. Since these methods of vulnerability assessment rely mostly on a comprehensive investigation of post-earthquake damage data, we classify them as empirical or qualitative approaches. Such empirical seismic vulnerability methods are usually classified as the rapid visual screening (RVS) method with different criteria for each country and the vulnerability index (National Group of Défense against earthquakes (GNDT) and European Macroseismic methods). The uncertainties associated with empirical vulnerability assessment are classified as follows: i. Variation in response of similar structure for the same intensity measure (IM). ii. Incorrect classification of the observed damage. iii. Variation in the geometrical qualities, material attributes, and seismic design of buildings that belong to the same class. iv. Insufficient numbers of observations for a certain building class, as well as incorrect identification of potential dangers. v. Inconsistency in weighting parameters between experts and specialist. vi. Need for sufficient or large input of data with different assessments and interpretations. vii. Selection of IM. viii. Selection of different Ground Motion Prediction Equation (GMPE). ix. Variation in repair technique costs and replacement cost as a damage loss. Therefore, in order to accomplish this goal, there are a number of cutting-edge methods for assessing seismic risk. These techniques aid in the evaluation of building damage and structural performance, whether it is for a single structure or a whole city. The vulnerability index method is one of these approaches. This method can be used to indicate the level of harm that has been done to a large number of structures or even just one building. This kind of vulnerability assessment is known as an empirical or qualitative technique, and it is dependent on a comprehensive review of post-earthquake damage data as well as the opinion of specialists. The vulnerability index (GNDT and European Macroseismic) methods have been successfully implemented in numerous applications in several European cities, including Barcelona, Bitola, Bucharest, Catania, Nice, Sofia, Thessaloniki, and other cities in the European Union. Numerous cities around the world have implemented this methodology [ValenciaSpain (Basset and Guardiola 2020), Tlajomulco-Mexico (Preciado et al. 2020), Weinan-China (Liu et al. 2020), Liera-Portugal (Blyth et al. 2020), Mexico CityMexico (Salazar and Ferreira 2020), Sant’Antimo-Italy (Chieffo et al. 2019), Ciutat Vella district in Barcelona-Spain (Lantada et al. 2018), Annaba City-Algeria (Athmani et al. 2018), Lorca-Spain (Ródenas et al. 2018; Aguilar-Meléndez et al. 2019), Azores-Portugal (Ferreira et al. 2017), Lampedusa Island-Italy (Cavaleri et al.

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2017), Morocco (Cherif et al. 2017, 2018), Switzerland (Lestuzzi et al. 2017); FaroPortugal (Maio et al. 2016), Nepal (Chaulagain et al. 2015), Athens-Greece (Eleftheriadou et al. 2014), Medellin-Colombia (Salgado-Gálvez et al. 2014), CologneGermany (Tyagunov et al. 2014), Almeria-Spain (Rivas-Medina et al. 2013), Hsinchu City Taiwan (Hung et al. 2013), Barcelona-Spain (Aguilar-Meléndez et al. 2012), Lisbon-Portugal (De Sá et al. 2012), Mérida-Venezuela (Castillo et al. 2011), Coimbra-Portugal (Vicente et al. 2011), Barcelona-Spain (Lantada et al. 2010), and Thessaloniki-Greece (Kappos et al. 2008)]. Yet, a thorough study requires a massive amount of input data, which may be impossible to gather in many developing countries. To give one example, consider Malaysia, where there is comparatively limited information on the observation of seismic events. The negative evaluation and a particular problem in the existing empirical method are the uniformity of the weighting values for structural feature parameters. This is assumed to be for RC buildings in the GNDT approach; however, various classification values were considered in other studies. Due to the fact that such a circumstance has led to a conflict in deciding what is of significance and what influences each weighting parameter in physical seismic behavior, this circumstance has led to uncertainties among researchers and authors regarding the identification of important parameters, which has led to discord among authors regarding the appropriate empirical approach. In addition, the empirical vulnerability index makes some assumptions, and combined with the simplified weighting of influential parameters, these assumptions have the potential to reduce the reliability and accuracy of the results of the vulnerability assessment for a particular susceptible building. The absence of an empirical method gives credibility and authority to the analytical method, which has the potential to improve the original vulnerability index method by taking into account the characteristics and parameters that influence the building’s response. These characteristics and parameters include the beam-column joint connection, the support boundary condition, the horizontal diaphragm system, the type of soil, the ductility level, horizontal and vertical mass irregularity, and material strength. It would appear that the majority of buildings in Malaysia belong to different clusters (low-, mid-, and high-rise) that have been planned in accordance with BS without paying sufficient consideration to the seismic conditions that are present in the nation (BS8110 1997). On the other hand, there is a lack of detailed data concerning the damage caused by post-earthquake aftershocks because catastrophic earthquakes do not occur very often. According to findings from earlier research, approximately half of the structures that have been inspected in Peninsular Malaysia exhibit signs of concrete deterioration and have vertical elements that do not possess sufficient levels of stiffness and strength (Adiyanto and Majid 2014). Recent events in East Malaysia provide conclusive evidence that the country falls into the low to moderate seismicity category and that future construction designs in the region should take into account the effects of earthquake loading (Majid et al. 2017). Hence, the concept of seismic resistance of Malaysian buildings is still not clear.

1.2 Indication of the Problem

9

After the terrible earthquake that struck Ranau, various RC structures, including residential buildings, schools, mosques, and temples, were found to have suffered damage. Among these structures were commercial buildings. Majid et al. (2017) explained that structural damage to RC-buildings caused by the earthquake was classified as soft-story failure mechanism, weak column-strong beam, lack of shear reinforcement in beams and columns, inadequate spacing of the shear reinforcement leading to shear failure, and poor concrete confinement. This information was based on a preliminary damage assessment of the 2015 Ranau earthquake. The categories of damage mentioned above were also reported by other studies (Ates et al. 2013; Romão et al. 2013; Tapan et al. 2013; Verderame et al. 2011; Bayraktar et al. 2013). Some seismic design recommendations and a methodology, like the SVI, were proposed as a result of these research, highlighting the urgent need to assess the seismic performance of existing structures and infrastructure in Malaysia. As a consequence of this, the SVI framework is based on modeling the parameters that influence the physical structural behavior without taking observations of previous damage into consideration. This is accomplished through an analytical approach and is accomplished by improving the vulnerability index method that was initiated in Italy and Europe. The vulnerability index approach necessitates a number of parametric evaluations, during which many conceivable vulnerability situations are taken into consideration, with the primary emphasis being placed on ERD, which validates the characteristics of the observed damage. Eventually, the results of this study have the potential to reduce the weight given to expert opinion in the future vulnerability assessments and aid in the management and implementation of solutions to reduce catastrophic losses from earthquakes. Plus, it can be a huge help to the authorities in charge of the restoration of older structures, such as those that are part of our cultural heritage.

1.2.1 Framework Objectives The completion of this study will result in the development of the SVI framework for RC-buildings in Malaysia utilizing a non-linear parametric approach, as well as its mean damage grade, which can be consistently applied regardless of the need for expert judgment. The primary goal of this framework is to develop an improved version of the original vulnerability index technique, which utilizes non-linear parametric analysis to quantify the physical structural characteristics that are the most effective overall (NL-PA).

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1.3 Conclusion This chapter focuses not only on the primary objective, but also on the issue that will be addressed as a result of putting this framework into action. This was achieved through creating an improved version of the previous vulnerability index strategy, which integrates the application of non-linear parametric analysis to accurately measure the physical structural features that are the most effective overall. This allowed for the achievement of the desired result. The conceptual approach is quickly becoming the new preferred method for modeling as it considers the universal precautions that need to be paired with the availability of noticeable damage attributes. In achieving this, the engineers of the structure can analyze and design parameters that greatly affect the structure’s behavior, and they can also develop a framework by generating an analytical SVI.

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K. Megawati, T.C. Pan, K. Koketsu, Response spectral attenuation relationships for Singapore and the Malay Peninsula due to distant Sumatran-fault earthquakes. Earthq. Eng. Struct. Dyn. 32(14), 2241–2265 (2003) N. Mendes, P.B. Lourenço, Seismic vulnerability of existing masonry buildings: nonlinear parametric analysis. Comput. Methods Appl. Sci. 37, 139–164 (2015) Z.V. Milutinovic, G.S. Trendafiloski, Risk-UE An advanced approach to earthquake risk scenarios with applications to different European towns, in Contract: EVK4-CT-2000-00014, WP4: Vulnerability of Current Buildings (2003) T.C. Pan, K. Megawati, Estimation of peak ground accelerations of the Malay Peninsula due to distant Sumatra earthquakes. Bull. Seismol. Soc. Am. 92(3), 1082–1094 (2002) J.W. Pappin, P.H.I. Yim, C.H.R. Koo, An approach for seismic design in malaysia following the principles of Eurocode 8, in IEM Jurutera Magazine, October (2011) H. Parra, M.B. Benito, J.M. Gaspar-Escribano, Seismic hazard assessment in continental Ecuador. Bull. Earthq. Eng. 14(8), 2129–2159 (2016) M.D. Petersen, S. Harmsen, C. Mueller, K. Haller, J. Dewey, N. Luco, A. Crone, K. Rukstales, D. Lidke, New USGS Southeast Asia seismic hazard maps, in The 14th World Conference on Earthquake Engineering, Beijing, China, October (2008) A. Preciado, A. Ramirez-Gaytan, J.C. Santos, O. Rodriguez, Seismic vulnerability assessment and reduction at a territorial scale on masonry and adobe housing by rapid vulnerability indicators: the case of Tlajomulco, Mexico. Int. J. Disas. Risk Reduct. 44, 101425 (2020) M.T. Ramírez-Herrera, N. Corona, A. Ruiz-Angulo, D. Melgar, J. Zavala-Hidalgo, The 8 September 2017 tsunami triggered by the M w 8.2 intraplate earthquake, Chiapas, Mexico. Pure Appl. Geophys. 175(1), 25–34 (2018) A. Rivas-Medina, J.M. Gaspar-Escribano, B. Benito, M.A. Bernabé, The role of GIS in urban seismic risk studies: application to the city of Almería (southern Spain). Nat. Hazard. 13(11), 2717 (2013) G. Rizzano, I. Tolone, Seismic assessment of existing RC frames: probabilistic approach. J. Struct. Eng. 135(7), 836–852 (2009) J. Rodenas, S. Garcia-Ayllon, A. Tomas, Estimation of the buildings seismic vulnerability: a methodological proposal for planning ante-earthquake scenarios in urban areas. Appl. Sci. 8, 1208 (2018) X. Romão, A.A. Costa, E. Paupério, H. Rodrigues, R. Vicente, H. Varum, A. Costa, Field observations and interpretation of the structural performance of constructions after the 11 May 2011 Lorca earthquake. Eng. Fail. Anal. 34, 670–692 (2013) M. Roohi, E.M. Hernandez, Performance-based post-earthquake decision-making for Instrumented Buildings. J. Civ. Struct. Heal. Monit. 10(3), 1–18 (2020) F. Salazar, L. Gerardo, T.M. Ferreira, Seismic vulnerability assessment of historic constructions in the downtown of Mexico City. Sustainability 12(3), 1276 (2020) M.A. Salgado-Gálvez, D. Zuloaga-Romero, G.A. Bernal, M.G. Mora, O.D. Cardona, Fully probabilistic seismic risk assessment considering local site effects for the portfolio of buildings in Medellín, Colombia. Bull. Earthq. Eng. 12(2), 671–695 (2014) S.Z. Sooria, S. Sawada, H. Goto, Proposal for seismic resistant design in Malaysia: assessment of possible ground motions in Peninsular Malaysia. Ann. Disas. Res. Ins. 55B (2012) M. Tapan, M. Comert, C. Demir, Y. Sayan, K. Orakcal, A. Ilki, Failures of structures during the October 23, 2011 Tabanlı (Van) and November 9, 2011 Edremit (Van) earthquakes in Turkey. Eng. Fail. Anal. 34, 606–628 (2013) C.G.N.D. Terremoti, Rischio sismico di edifici pubblici Parte II Risultati per la regione EmiliaRomagna (CNR Gruppo Nazionale Difesa Terremoti, Roma, 1993) S. Tyagunov, M. Pittore, M. Wieland, S. Parolai, D. Bindi, K. Fleming, J. Zschau, Uncertainty and sensitivity analyses in seismic risk assessments on the example of Cologne, Germany. Nat. Hazard. 14(6), 1625–1640 (2014) UNISDR, Sendai Framework for Disaster Risk Reduction 2015–2030 (2015) United States Geological Survey (USGS), Seismic Hazard of Western Indonesia (2008)

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G.M. Verderame, F. De Luca, P. Ricci, G. Manfredi, Preliminary analysis of a soft-storey mechanism after the 2009 L’Aquila earthquake. Earthquake Eng. Struct. Dynam. 40(8), 925–944 (2011) R. Vicente, S. Parodi, S. Lagomarsino, H. Varum, J.M. Silva, Seismic vulnerability and risk assessment: case study of the historic city centre of Coimbra, Portugal. Bull. Earthq. Eng. 9(4), 1067–1096 (2011) B. Yon, Seismic vulnerability assessment of RC buildings according to the 2007 and 2018 Turkish seismic codes. Earthq. Struct. 18(6), 709–718 (2020) J.Z. Zhang, J. Jiang, G.Q. Li, An improved consecutive modal pushover procedure for estimating seismic demands of multi-storey framed buildings. Struct. Design Tall Spec. Build. 26, e1336 (2017)

Chapter 2

Seismic Vulnerability Index Approach

2.1 Introduction This chapter outlines a proposed simplified technique for the seismic risk evaluation of reinforced concrete (RC) buildings in Malaysia, which is based on the methodology of the Seismic Vulnerability Index (SVI). The process that is utilized is adapted, with minor adjustments, from the Italian approach known as “The National Group of Defense” (GNDT), which is used to defend against earthquakes, as well as the European approaches known as “Macroseismic.” Estimating the vulnerability indices of RC structures required modeling eight parameters within three unique vulnerability classes. These parameters were then used. The Earthquake Resistant Design (ERD) definition is used to categorize the vulnerability classes. The ERD definition is as follows: (low, moderate, and high). In order to compute the SVI, a Non-Linear Dynamic Analysis (also known as NL-DA) and a Non-Linear Static Analysis (also known as NL-SA) were carried out. After determining the SVI, the mean damage states, fragility curves, and vulnerability curves based on cumulative and beta distribution functions were developed to evaluate the estimated physical damages to buildings under distinct seismic intensities, that in role quantify the selected damaged area in the form of 3-D mapping using Geographical Information System (GIS) platform as a tool for indicating the mitigation and rehabilitation process.

2.1.1 The New Seismic Vulnerability Index (SVI) Approach The proposed approach aim is to increase the accuracy of the empirical vulnerability index. This will be accomplished by creating a new SVI through the application of an analytical method. This method involves carrying out a non-linear parametric analysis (NL-PA) by simulating a set of earthquake records using a variety of different © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. M. Kassem and F. Mohamed Nazri, Seismic Vulnerability Index Assessment Framework of RC Structures, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-981-99-5038-6_2

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Near-Field (NF) and Far-Field (FF) seismic scenarios. This is an alternative to decisions and judgements made by specialists in the field. The phases that were utilized in the research methodology are represented in the following paragraphs, and Fig. 2.1 provides a flowchart of the research methodology. Stage 1: Statistics and a data collection will be acquired in the first stage, which will be used to quantify the 30 RC-buildings. Stage 2: Parameters for seismic vulnerability (P1–P8) are assigned and designed using established methods. Stage 3: Breaking down earthquake hazards into three distinct ERD categories: (low, moderate, and high). Stage 4: The Non-Linear Time History Analysis (NL-THA) is applied to a database of synthetic seismic records generated using a Non-Linear (NL) simulation platform (NL-THA). Stage 5: Determine the specifications for the weighting coefficients based on the top displacement of the structure as an Engineering Demand Parameter (EDP). Stage 6: Calculating the SVI with the NL-DA, which can take values from 0 to 1, (less vulnerable to the most vulnerable). Stage 7: Estimating the SVI by the use of the Non-Linear Static Analysis (NL-SA), which is based on the creation of plastic hinges in beams and columns. Stage 8: Quantification of mean damage grades, as well as the generation of fragility or vulnerability curves for each of the five damage levels (D1, D2, D3, D4, and D5), as well as for the Near Collapse (NC) limit states that are stated in the Vision 2000 guidelines. Stage 9: Create an SVI form for surveying, as well as a GIS schema for average damage grades and SVI for each of the three seismic situations and intensities (VII, VIII, and IX).

2.2 Strategy Adopted for Selecting the Modeling Parameters In order to develop a new approach for defining and calibrating the structural characteristics that influence the vulnerability of RC-buildings, a number of parametric analyses have to be carried out. These analyses were carried out in order to design the new methodology. It was observed that a variety of parameters have an effect on the structural vulnerability in terms of physical safety and threat. This information was used to inform the development of the new methodology. In this study, eight modeled factors were chosen, and the same technique was followed, with minor alterations according to the GNDT. These parameters were

2.2 Strategy Adopted for Selecting the Modeling Parameters

Fig. 2.1 Process flow diagram of the proposed methodological strategy

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Fig. 2.2 Choosing the SVI-modeled parameters for the analytical method

then categorized into three vulnerability classes, specifically the (low, moderate, and high) ERD classes. When the NL-PA was applied, the eight modeled parameters (P1– P8) had a direct effect on the structural behavior. This was achieved without taking the observations of previous damages into consideration (NL-PA). The selection of parameters in accordance with the initial methodology is illustrated in Fig. 2.2. The specifics of each parameter are presented and discussed in the following sections.

2.2.1 Beam-Column Joint Connection (P1) One of the most important parameters in an earthquake resistance building is having a suitable beam-column connection joint design, which must be adequately designed to resist earthquake effects. There are two major problems that must be considered in a beam-column joint. The first is related to insufficient bonds between longitudinal bars and the concrete core, and the second is related to the lack of transverse rebars that might cause shear failure in the joint with diagonal cracking, which leads to concrete spalling and crushing. In seismic design philosophy, the strong column-weak beam design is required to avoid the soft-story mechanism and ensure that the beam develops plastic hinges

2.2 Strategy Adopted for Selecting the Modeling Parameters

19

before the columns. The beam-column joints detailing is mentioned in most international building code provisions, which basically refer the amount on transverse reinforcement and its confinement to resist horizontal shear forces, in addition to other limitations such as, spacing between hoops, closed hoops at hinging regions, bonding development length, overlapping splices, and other reinforcement regulations. Researchers require models that can assess the behavior of joints under seismic loading to evaluate the performance of existing RC-buildings (Ganasan et al. 2020; Parate and Kumar 2019). This parameter (P1) is modeled according to the flexibility of the connection, by defining the rigid offset length. Rigid offset length models can simply implement to Finite Element (FE) software, which simulates the joint flexibility at the end of beams and columns where the physical joint region is represented. Thus, by adjusting the rigid offset length, the joint can be made flexible, semi-rigid, or rigid. In the proposed non-linear numerical model, the plastic hinges simulation is signified as non-linear flexural behavior of the beams, which is also provided for the top and bottom columns from the joint interface. The stiff offset length is taken into account while modeling the beam-column joint connection characteristic known as P1. In the Low-ERD class, there is no consideration given to the rigid offset length, and as a result, there is no allowance for rotation with the releasing moment. This type of connection is known as simple shear. The parameter is treated as a fully rigid or partially constrained joint with an offset length of 0.5 and 1.0, respectively, in the Moderate-ERD and High-ERD classes. In both of these classes, the ERD value is considered to be moderate (Birely et al. 2010; Parate and Kumar 2018). Figure 2.3 describes the end rigid zones for modeling beam-column connection, (a): flexible connection (β = 0), (b): fully rigid connection (β = 1), and (c): with semi-rigid connection (β = 0.5).

2.2.2 Boundary Conditions (P2) The boundary condition is one of several aspects that influence the structures’ energy dissipation capacity, which is modeled in accordance with the underlying support conditions. This building was designed with a hinged support system in the Low-ERD category. This is in contrast to the High-ERD class, in which all of the supports are regarded to be fixed or fully restrained. In the Moderate-ERD class, two different scenarios were taken into consideration. In the first scenario, the supports are outwardly hinged and internally fixed, but in the second scenario, the supports are externally hinged and internally fixed (Irheem and Attia 2017). Figures 2.4a, b illustrate the different types of footings used for fixed and hinged boundary conditions.

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Fig. 2.3 Modeling end rigid zone (β) for the vulnerability classes, a flexible connection, b fully rigid connection, and c semi-rigid connection

2.2.3 Horizontal Diaphragm System (P3) A diaphragm is a horizontal planar system that transmits lateral loadings to the vertical parts. Additionally, it sustains gravity loads in the event that the structure bends out of plane and the uplift forces that are induced by earthquakes’ vertical acceleration. When subjected to seismic loading, RC floors perform the function of a diaphragm by distributing the inertial forces that are created by an earthquake to the elements of the frame and walls that are resisting the earthquake. The flexibility and rigidity of the floor are both defined by the relative stiffness of the floor diaphragm in comparison with the stiffness of the vertical resisting elements. This dictates how shear forces are transferred to the lateral members. The behavior of the floor as a diaphragm is a key factor in the transfer of shear forces when it is subjected to seismic loadings. The in-plane stiffness of the floor. The diaphragms can be broken down into three distinct categories based on their relative degrees of flexibility: rigid, flexible, and semi-rigid. It is rigid, when it is relatively stiffer than the lateral resisting elements, so no bending deformation is expected in the diaphragm plan. In addition, all the members connected to the diaphragm are displaced similarly to diaphragm displacement, and the lateral shear forces are distributed in direct proportion based on the rigidness of the resisting elements. Instead, it can be assumed flexible, if it is less stiff than the

2.2 Strategy Adopted for Selecting the Modeling Parameters

21

Fig. 2.4 Boundary conditions and support types, a fixed support and b hinged support

lateral resisting elements, whereby in this case, each frame acts independent to the others based on its loaded tributary areas. The parameter for the horizontal diaphragm floor system is modeled by taking into account three different types of diaphragms: flexible, semi-rigid, and rigid. These diaphragms are then distributed into three different vulnerability classes, ranging from the most vulnerable (Low-ERD) to the least vulnerable (High-ERD). The floor is modeled as a flexible diaphragm in the Low-ERD class, a rigid diaphragm in the High-ERD class, and a semi-rigid diaphragm in the Moderate-ERD class. In the Moderate-ERD class, the floor behaves as a semi-rigid diaphragm.

2.2.4 Type of Soil (P4) It is vital to determine the dynamic qualities of the soil when working in the field of earthquake engineering. Ignoring this parameter and failing to have a solid understanding of the geological features of the site, both have the potential to result in

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2 Seismic Vulnerability Index Approach

structural damage and failure. This parameter is significant for both the analysis of site reaction and the design of structures. This parameter is modeled by considering the soil as a series of spring supports (for low-rise and mid-rise buildings), and the translational and rocking properties of the springs are taken from FEMA 356, as illustrated in Fig. 2.5. This modeling technique is used for both types of buildings. Calculating the spring stiffness involves determining the stiffness translation and rocking factors of Federal Emergency Management Agency 356 (FEMA 356 2000) and using the data from Table 2.1 to make the appropriate calculations. In addition, the seismic recordings are scaled based on soil type to simulate this parameter that is related to National Annex (NA) provisions in Sabah where the validation buildings allocated as the most excited seismic zone (0.165 g) in Malaysia. While for the rest of the 28 selected buildings as a data collection, the provisions of Penang are used for scaling the modeled parameters according to its soil types (B, C, and D) according to NA. Tables 2.2 and 2.3 illustrate the soil parameters in Sabah and Penang for each ground type, which that are distributed into three ERD vulnerability classes. The aim of this research work is to provide an assessment of the effect of soil interaction type during seismic loading on RC-buildings. The stiffness is typically calculated by a geotechnical engineer and then used by a structural engineer.

Fig. 2.5 Illustration in the form of a schematic showing soil springs being used to sustain the frame structure while it is being subjected to earthquake loading

2.2 Strategy Adopted for Selecting the Modeling Parameters

23

Table 2.1 Spring stiffness for soil-structure interaction (SSI) in FEMA356 Degree of freedom (DOF)

Spring stiffnesses   0.65  GB 3.4 BL K x = 2−ν + 1.2

Equation number

Ky =   0.65    GB L + 0.4 BL + 0.8 2−ν 3.4 B    0.75 GB 1.55 BL Kv = 1−ν + 0.8    0.75 3 L Kr x = GB + 0.1 1−ν 0.4 B    L 2.4 3 + 0.034 Kr y = GB 1−ν 0.47 B    2.45 Kr z = G B 3 0.53 BL + 0.51

(2.2)

Shear Modulus (G)

G = ρs × Vs2

(2.7)

Density of soil (ρs )

ρs = 0.44 × Vs0.25 qall = 2.4(10−4 )ρs Vs

(2.8)

Translation along x-axis Translation along y-axis

Translation along z-axis Rocking about x-axis Rocking about y-axis Torsion about z-axis

(2.1)

(2.3) (2.4) (2.5) (2.6)

Other soil parameters (G, ρs , qall )

Allowable bearing capacity (qall )

(2.9)

Table 2.2 Soil type parameters in Sabah, Malaysia (Malaysia NA 2017) Sabah ground type

S

T B (s)

T C (s)

T D (s)

ERD class

B

1.40

0.150

0.400

2.00

High

C

1.35

0.150

0.600

2.00

Moderate

D

1.35

0.200

0.800

2.00

Low

Table 2.3 Soil type parameters in Penang, Malaysia (Malaysia NA 2017) Penang ground type

S

T B (s)

T C (s)

T D (s)

ERD class

B

1.4

0.050

0.200

2.20

High

C

1.15

0.050

0.500

2.20

Moderate

D

1.35

0.300

0.800

2.20

Low

2.2.5 Ductility Level (P5) Multiple normative regulations classify the ductility level principle as a criterion for behavior. In the American code, referred to ACI318-14 (ACI 2014), it is called a response modification coefficient (R), while in the European code, referred to Eurocode 8 (EC8 2004), it is called a behavior factor (q). The earthquake codes specified three ductility classifications for the Reinforced Concrete Moment Resisting Frame so that buildings may be ranked from least ductile to most ductile according to their resistance to earthquakes.

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2 Seismic Vulnerability Index Approach

To this end, the ductility of RC-buildings is described and modeled in terms of three distinct ductility classes, much like the behavior factor q presented in EC8. For a moment resisting frame, the Ductility Class Low (DCL) is designated for a behavior factor of less than 1.5, representing a Low-ERD vulnerability class; the Ductility Medium Class (DCM) is designated for a behavior factor of 1.5–4.0; and the Ductility Class High (DCH) is designated for a behavior factor of 4.0 or greater.

2.2.6 Horizontal and Vertical Mass Irregularity (P6 and P7) An irregular building is one that does not have a uniform distribution of mass, stiffness, geometry, in-plane discontinuity, or any of the other structural attributes that are specified in international seismic codes. The irregularities can be broken down into four categories, as shown in Fig. 2.6a–d, which are labeled as follows: (a) stiffness irregularity (SI), (b) mass irregularity (MI), (c) geometric irregularity (GI), and (d) in-plane discontinuity (ID). For the purpose of this investigation, the MI of the chosen RC-buildings is taken to represent both the vertical and horizontal mass irregularities. The modeling of this parameter was accomplished by taking into consideration the mass ratios, mr, in two distinct locations within the top and bottom of a certain building. The mass ratio is defined as the massive floor ratio over the neighboring floor weight (Karavasilis et al. 2008; Sadashiva et al. 2009; Bhosale et al. 2018). If the mass of any story exceeds that of the adjacent floors by a factor of 1.5, an investigation into the possibility of a mass irregularity needs to be carried out in accordance with the regulations of the Uniform Building Code of 1997 (UBC97). Because of this, the irregularities are classified according to one of three distinct mass ratios: either 2, 4, or 6. The 2mT is the terminology mr mlocation that corresponds to a building that has a mass ratio that is equal to 2 on the top floor, as demonstrated in

Fig. 2.6 Vertical irregular building frame in 4 types, a SI, b MI, c GI, and d ID (Bhosale et al. 2018)

2.2 Strategy Adopted for Selecting the Modeling Parameters

25

Fig. 2.7 Regularity and irregularity of structure’s mass floor distribution

Table 2.4 Vulnerability classification for the mass irregularity parameter Vulnerability classes

Mass ratios

Mass location

Low-ERD

2,4, and 6

Top

Moderate-ERD

2,4, and 6

Bottom

High-ERD

Regular mass (mr = 1)

Regularly distributed

Fig. 2.7. Table 2.4 provides a visual representation of the mass irregularity’s spatial distribution, which is organized according to vulnerability classifications.

2.2.7 Material Strength (P8) The design of the mixture, as well as the strength of the concrete, is two of the primary factors that influence the physical structural vulnerability. Consequently, this characteristic is modeled to define concrete strength grades in accordance with the three different vulnerability classes. Aside from this analysis, it is more recommended to specifically specify the concrete strength in accordance with the standards that govern earthquake design. The regulations of the American Concrete Institute (ACI) state that the strength of concrete is considered to be normal if the value is 2500 psi (16 MPa) or below; however, for ERD, the value must be 5000 psi (35 MPa) or greater (ACI 2014). As a result, the criterion for concrete strength that was chosen

26

2 Seismic Vulnerability Index Approach

for this investigation is separated into three grades, such as 16, 25, and 35 MPa for the Low-, Moderate-, and High-ERDs vulnerability classes, respectively.

2.3 Ground Motions Records Selecting sufficient ground motion data is essential to achieving the NL-THA, which is one of the most crucial factors for valid dynamic analysis. The Consortium of Organizations for Strong-Motion Observation Systems Database (COSMOS) and Pacific Earthquake Engineering Research (PEER) Center provide ground motion records that meet international code criteria. Several characteristics, such as the magnitude (Mw), Peak Ground Acceleration (PGA), distance from the source (NF or FF), and soil type, should be addressed when choosing ground motions, as demonstrated by Wang (2011), Nazri and Alexander (2012), and Villar-Vega et al. (2017). As a result of the fact that the majority of buildings in Malaysia were constructed on soft soil conditions, which is suitable with soil type D (Adiyanto and Majid 2014), it has contributed to NF ground motions with an epi-central distance < 20 km due to its high seismic pulse that could affect the index value. In order to acquire all of the possibilities, it is obvious that a diverse broad range of Peak Ground Acceleration (PGA) intensities should be taken into consideration. In spite of this, Malaysia is located on a steady portion of the Eurasian plate; yet, its buildings are built on weak soil and are susceptible to tremors caused by earthquakes in the FF that occur in Sumatra.

2.4 Earthquake Resistant Design Concept-Based Vulnerability Classes Numerous scholars have investigated the concept of “physical vulnerability” because to its importance as a piece of evidence for the application of ERD and the disaster prevention and mitigation principles it entails. By utilizing the ERD concept to model each parameter, an improved SVI was created, which takes into account the following three types of seismic vulnerability: i. Low-ERD class: The structures do not meet seismic rules and have poor seismic performance. ii. Moderate-ERD class: The structures resist seismic loading moderately. iii. High-ERD class: The structure is seismically designed (high ductility expected). Table 2.5 summarizes the approach taken in modeling the parameters for each vulnerability class.

2.5 Analytical Method for an Improved Seismic Vulnerability Index

27

Table 2.5 ERD vulnerability class-based modeling parameters Parameters

Earthquake resistant design vulnerability classes Class 1 L-ERD

Class 2 M-ERD

Class 3 H-ERD

P1

Flexible joint

Partially rigid joint

Fully rigid joint

P2

Hinged restraints

1st case

2nd case

Contains both an externally fixed and an internally hinged one

Contains both an external hinge and an internal fixation support

Fully restricted supports

P3

Flexible diaphragm

Semi-rigid diaphragm

Rigid diaphragm

P4

Soil type D

Soil type C

Soil type B

P5

q < 1.5

1.50 < q < 4.0

q>4

P6 and P7

mr mlocation 2mTop, 4mTop, 6mTop

mr mlocation 2mBottom, 4mBottom, 6mBottom

mr = 1.0

P8

16 N/mm2

25 N/mm2

35 N/mm2

2.5 Analytical Method for an Improved Seismic Vulnerability Index Because to the uncertainty generated from the restricted data in the empirical methodologies, NL-DA and NL-SA are utilized to validate the fragility features discovered in the field. This technique does not rely on either past damage observations or subjective human screening assessments; instead, it is included within a modeling of the structural parameters. This is an example of creating and modeling quantitative characteristics for the purpose of calculating a vulnerability index based on the required number of parametric assessments; the focus here is on ERD. Therefore, the improvement of the SVI is based on evaluating each modeled parameter independently throughout different transmission phases that correspond to the vulnerability classes; the initiation phase: from Low-ERD to Moderate-ERD, and the second phase: from Moderate-ERD to High-ERD. This can be accomplished by employing the Incremental Dynamic Analysis (IDA) and the fragility curves by giving the EDPs for the quantitative measurements, as shown in Fig. 2.8.

2.5.1 Incremental Dynamic Analysis Non-Linear Time History Analysis (NL-THA) is used for dynamic analysis and requires a suitable set of ground motions records to develop the Incremental Dynamic Analysis (IDA) curves. IDA is a useful parametric analysis tool that has recently

28

2 Seismic Vulnerability Index Approach

Nonlinear Dynamic Analysis (NL-DA)

Distributed into 3 Vulnerability Classes

1st Transmision Stage (Low-ERD to Moderate-ERD)

Specified Parameter (P1 to P8) to Analyse

Maximum Displacement as a Damage Measure (DM)

2nd Transmission Stage (ModerateERD to HighERD)

Apply Seismic Ground Motion Records

Incrementally Increase the Intensity Measure (IM)

Parameter Quantification using IDA and Fragility curves at NC Limit State

Fig. 2.8 Flowchart for categorizing each parameter through two steps of transmission

appeared in various forms for evaluating the seismic performance of a structure. Damage measure (DM) and intensity measure (IM) are used as the main indicators. Nevertheless, a suitable measure of ground motion intensity must be specified in order to construct IDA curves that accurately and efficiently reflect the quality of the system related to different earthquake records with that intensities. The DM shows that the maximum displacement that can be converted into Inter-Story Drift Ratio (ISDR) in which the performance of the structure is attributed to its performance level, and the IM is based on the spectral acceleration (S a ) for the vibration period (T 1) which is related to the critical deformed shape in the structure’s 1st mode shape. While the first mode spectral acceleration is an accurate index for structures to respond elastically, it is often used as a default earthquake intensity scaling parameter for time-history analysis. Since the spectral acceleration at fundamental period S a (T 1) is a convenient and efficient intensity measure for first mode-dominated structures (Shome et al. 1998; Cordova et al. 2000), it has been widely adopted in seismic design codes and research in many countries. Later, the IDA curves were plotted based on the relation of (DM vs. IM) to provide an overview of the structure’s seismic behavior subjected to earthquakes movements.

2.5.1.1

Developing a Seismic Vulnerability Index Via Non-linear Analysis of Past Events

Each of the chosen RC-buildings had its weighting parameters calculated using a series of Non-Linear Time History Analyses (NL-THA). However, the maximum top displacement is assigned as an EDP to explicitly evaluate or quantify the

2.5 Analytical Method for an Improved Seismic Vulnerability Index

29

building’s vulnerability in order to develop the weighing coefficient for each parameter (EDP). This allows the assessment of how often factors affect structure susceptibility and seismic event response. The following steps are employed to calculate the weights used in estimating a particular building’s SVI: Step 1: Calculate the maximum top displacement capacity ratio for each vulnerability class (K i ), where Dmax represents maximum displacement, as shown in Eq. (2.1). Dmax K i = ∑i=3 i=1 Dmax

(2.1)

Step 2: Calculate the average factor with respect to a number of seismic records (K L ), where N represents the number of seismic records as in Eq. (2.2). ∑i=n

KL =

Ki Number of Seismic Records, N i=1

(2.2)

Step 3: Normalizing the final weighting factor (K n ) by dividing the (K L ) values for each parameter over the sum of K L factors obtained in the vulnerability class (Low-ERD) as shown in Eq. (2.3). ∑i=n

Kn = ∑

i=1 K i K L class Low-ERD

(2.3)

Finally, from the normalized factor obtained in Step 3, the SVI for each vulnerability classes (Low-ERD, Moderate-ERD, and High-ERD) is calculated using Eq. (2.4), where (n) represents the number of modeled parameters (n = 1, 2, 3, 4, 5, 6, 7, and 8) in this study. Afterward, the SVI with ± σ and ± 2σ was well calculated to consider the effect of uncertainties in the final results. SVI =

2.5.1.2

∑n= j n=i

Kn

(2.4)

Developing Vulnerability Index Based on Non-linear Static Analysis

Damage to buildings during an earthquake can be measured using vulnerability index (VINLSA ). It is defined based on the weight factor of the frame elements (beams and columns), as well as the plastic hinge formations caused by its performance levels. From the plastic plateau (B–C) in the load-deformation curve, it can be subdivided into several range of performances, such as B-IO, IO-LS, LS-CP, CP-C, D-E, and > E.

30 Table 2.6 Performance levels weightage factors

2 Seismic Vulnerability Index Approach

Serial number

Performance level

Weightage factor (x i )

1

E]

1.00

Following the completion of the pushover study, the vulnerability index is used to determine the number of hinges that have been produced inside the frame elements of each performance level. In addition to the analysis, a weight factor (xi ) is allocated to each performance level, and the results of this assignment may be found in Table 2.6. When it comes to the column element, the importance factor is “1.5,” but when it comes to the beam element, it is equivalent to “1.0.” This is due to the fact that the global safety in the columns should be greater than what is present in the beams. In order to determine a building structure’s vulnerability index (VINLSA ), one uses the expression that is provided below: VINLSA =

1.5

∑ ∑ c Ni xi + 1.0 Nib xi ∑ c ∑ b Ni + Ni

(2.5)

where Nic and Nib refer to the number of plastic hinges established in columns and beams, respectively, “1.5” and “1.0” are the importance factors in the column element and beam element, respectively, and xi is the weighting coefficients for each performance level.

2.5.2 Vulnerability Classifications of RC-Buildings Damage is categorized as negligible, minor, moderate, partial collapse, or total collapse based on the vulnerability index values determined by the two methods (NL-THA and NL-SA). For vulnerability index values between [0.10] and [0.20], the building is classified as Green 1 and represents negligible to light damages; for values between [0.20] and [0.40], the building is classified as Orange 3 and represents moderate to heavy damages; for values between [0.55] and [0.70], the building is classified as Orange 4 and represents partial collapse; and for values between [0.70] and [1.0], the building is classified as Red 5 and represents total collapse. Still, as shown in Tables 2.7 and 2.8, there is a correlation between the vulnerability categories and the actual damage.

2.6 Probabilistic Damage Distribution

31

Table 2.7 Classification of the vulnerability of reinforced concrete buildings based on their SVI scores (Belheouane and Bensaibi 2013) Vulnerability levels

Green

Orange

Red

1

2

3

4

5

SVI

[0.10–0.20]

[0.20–0.40]

[0.40–0.55]

[0.55–0.70]

[0.70–1.00]

SVI, mean

0.150

0.300

0.475

0.625

0.850

Table 2.8 Vulnerability categories according to the observed damage (Belheouane and Bensaibi 2013) Damage categories

Levels

Description

Negligible

Green 1

Negligible/light damage

Minor

Green 2

Light to moderate

Moderate

Orange 3

Moderate to heavy

Serious

Orange 4

Heavy

Collapse

Red 5

Close to total collapse or total collapse

2.6 Probabilistic Damage Distribution 2.6.1 Mean Damage State Estimating the mean damage grade associated with a building is vital for choosing a RC structure, and this may be determined using the SVI, which was described before. According to the European Macroseismic methodology, the attribution of the mean damage grade was estimated by defining five different damage grades. These damage grades were labeled as light, moderate, substantial to heavy, very heavy, and total destruction and were designated with D1, D2, D3, D4, and D5, respectively. Table 2.9 classifies the RC-building damage grades, and Fig. 2.9 describes the correlation needed to validate between the estimated SVI, mean damage state μD , and the observational damage features with respect to the intensity measure (IM). A mean vulnerability function is expressed to correlate seismic hazards with mean damage grades (0 < μD < 5) of the RC-building in a relation with the SVI, as shown in Eq. (2.6).

  I + 7 × SVI − 13 μD = 2.55 × 1 + tanh Q

(2.6)

where I describes the seismic intensity related to the European Macroseismic Scale 1998 (EMS-98), SVI is the computed SVI, and Q is the ductility factor of the construction’s typology ranging from 1 to 4, which it is assumed to be 1 in this work. This is because the Malaysian construction industry follows the British Standard (BS8110) as a reference for RC design that is not situated in active seismic fault

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Table 2.9 Classification system for the severity of damage to reinforced concrete structures Reinforced concrete building damage grades D1

Negligible or slight damage. Small cracks in the plaster over the frame or at the bottom of the walls. Partitions and fills have small cracks

D2

Moderate damage. Fractures in the walls of the structure, as well as the beams and columns of the frame. Fractures in the partitions and infills, crumbling mortar, and dropping plaster from the joints of the wall panels are all signs of structural damage

D3

Substantial to heavy damage fractures in the beam-column joints of the columns of the frames at the base, as well as the joints of the coupled walls. Cracking of the concrete cover and buckled reinforced rods. Numerous individual infill panels and partitions have failed, and there are numerous large fissures throughout the structure

D4

Very heavy damage. A lot of damage. Large cracks in the building’s parts, the collapse of a few columns or a single upper floor, and the separation of the bars that hold the beam together

D5

Total collapse or close to collapse

Damage Analysis of Selected RC-Building

Mean Damage Grade

RC building SVI classifications Green Level: 0.15 < Orange Level: 0.475 < Red Level: 0.625