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Seismic risk prioritization of masonry building stocks using machine learning

EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS 2024年第14期53卷 4432-4450页

作者： Coskun, Onur Aktepe, Rafet Aldemir, Alper Yilmaz, Ali Erhan Durmaz, Murat Erkal, Burcu Guldur Tunali, Engin Hacettepe Univ Dept Civil Engn Ankara Turkiye Hacettepe Univ Dept Geomat Ankara Turkiye

The seismic risk mitigation plans are vital since vulnerable structures are prone to partial or total collapse under the effect of future major earthquake events. Therefore, vulnerable structures in large building stocks should be determined using robust and accurate methods to prevent loss of lives and property. In the current state-of-the-art, the risk states (i.e., whether risky or not) of structures completely depend on the experience of the reconnaissance team of engineers, which could not result in standardized decisions. In this study, machine learning has been integrated into the decision-making algorithm to classify more precise and reliable seismic risk states of masonry buildings, categorizing them into up to four risk categories. For this purpose, a large database, including 12 features and detailed seismic risk analysis results of 4356 masonry buildings, is formed. Firstly, the input variables are preprocessed using feature engineering methods. Then, several machine learning algorithms are utilized to produce a network to estimate the risk state of masonry buildings in association with the risk states obtained from the detailed analysis results. As a result of the analysis of these algorithms, the correct prediction percentages for the testing database of the proposed method for two, three, and four risk states classification are predicted as approximately 87.5%, 86.6%, and 79.0%, respectively. This new approach makes it possible to produce risk color maps of large building stocks and reduce the number of buildings that require immediate action.

关键词： machine learning masonry structures rapid visual screening seismic risk classification seismic risk prioritization

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Android-based frame-work for condition assessment of existing rc hospital buildings in Karachi

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PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-STRUCTURES AND BUILDINGS 2024年第1期178卷 73-93页

作者： Khan, Rashid A. Mohammad, Aslam F. Haseeb, Abdul Hammad, Muhammad Siddiqui, Muhammad Afnan Lodi, Sarosh H. NED Univ Engn & Technol Earthquake Engn Dept Main Univ Rd Karachi 75270 Pakistan NED Univ Engn & Technol Civil Engn Dept Main Univ Rd Karachi 75270 Pakistan

rapid visual screening (RVS) is a practically viable tool implemented all over the world to rank structures based on their seismic vulnerability. Seismic assessment of hospital buildings is essential in seismic-prone regions to ensure their structural integrity and readiness to provide emergency care and medical treatment after significant seismic events. Therefore, the current study developed a similar approach to the proven methodologies reported in the literature, such as ASCE, FEMA, WHO, GHI, etc., for the seismic evaluation of structural and non-structural elements of hospital buildings in a metropolitan city like Karachi. Karachi is located in the southwest of Pakistan and is vulnerable to earthquake and tsunami hazards. To this end, an Android-based RVS system has been developed for large-scale seismic risk mapping of hospital buildings. The proposed system acquires the physical status of the building features in real-time and identifies the building performance obtained from the calculated seismic risk index. Moreover, three case-study hospital buildings have been employed and analyzed through the proposed RVS system. Results illustrated the performance of case-study hospital buildings at organizational, structural, and non-structural levels based on seismic risk indices.

关键词： rapid visual screening seismic assessment condition assessment hospitals vulnerability index risk index seismic risk mapping

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Structural score and score modifiers to quantify seismic vulnerability of irregular buildings

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STRUCTURES 2025年 73卷

作者： Kumar, Rokalla Devi Santosh Singh, Saurabh Kumar Maity, Damodar Bhattacharya, Shankha Pratim Indian Inst Technol Dept Civil Engn Kharagpur 721302 India Indian Inst Technol Dept Architecture & Reg Planning Kharagpur 721302 India

High seismic-prone regions often face devastating structural failures during earthquakes, primarily due to irregular building configurations. To mitigate these risks, conducting vulnerability assessments before seismic events is crucial. While detailed structural analysis provides the most accurate evaluations, it is resource- intensive. Therefore, the present study focuses on obtaining structural scores and score modifiers for irregular buildings that are vulnerable to seismic events using the OSHPD HAZUS methodology. These scores and modifiers can then be readily applied in a generalized manner to conduct rapid visual screening (RVS) of similar buildings. The irregularities that have been considered are overhangs, eccentric beams, floating columns, diaphragm discontinuities, and short columns. The buildings have been modelled using SAP2000 software through pushover analysis. Further, the obtained score modifiers for each irregularity have been compared against existing RVS methods. This enhanced RVS approach provides a more accurate and resource-efficient evaluation of structural vulnerability in regions with a high seismic risk, accounting for degree of irregularity.

关键词： rapid visual screening Collapse probability OSHPD HAZUS Basic score Modifier FEMA P-155

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Enhanced Accuracy of a Fuzzy Logic Model for rapid Seismic Damage Prediction of RC Buildings

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KSCE JOURNAL OF CIVIL ENGINEERING 2024年第1期28卷 250-261页

作者： Zaribafian, Omid Pourrostam, Towhid Fazilati, Mehdey Moghadam, Abdolreza S. Pahlaviani, Ali G. Islamic Azad Univ Dept Civil Engn Cent Tehran Branch Tehran *** Iran Islamic Azad Univ Dept Civil Engn Najafabad Branch Najafabad *** Iran Int Inst Earthquake Engn & Seismol IIEES Tehran *** Iran

rapid visual screening (RVS) is a method of assessing a building's vulnerability to an earthquake. Although RVS accurately classifies the damage states of the studied buildings, the parameter values assigned by this method have intrinsic uncertainties that can highly reduce the accuracy of its predictions. Accordingly, using a fuzzy logic model is known to mitigate these uncertainties and offer a more reliable level of damage prediction. A recently-proposed Dual-Fitness Particle Swarm Optimization (DFPSO) algorithm is applied to fine-tune the hyperparameters of this model, named the FLM-DFPSO model. Furthermore, the 1994 Northridge earthquake data is used to benchmark the performance of the proposed model against a well-known model in the literature. The results suggest that the proposed model improves the performance criteria by 7.46% and 27% in the training and validation stages, respectively. The model also boosts the prediction accuracy by a rate of 53% in the validation step, confirming the FLM-DFPSO as a highly reliable model to learn and also generalize the relationship between its inputs and output to other cases when assessing seismic damage.

关键词： rapid visual screening Earthquake Damageability DFPSO Fuzzy logic Self-organizing map

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A Machine-Learning-Based Seismic Vulnerability Assessment Approach for Low-Rise RC Buildings

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JOURNAL OF EARTHQUAKE ENGINEERING 2024年第3期28卷 760-776页

作者： Elyasi, Niloofar Kim, Eugene Yeum, Chul Min Univ Waterloo Dept Civil & Environm Engn Waterloo ON Canada Univ Waterloo Dept Civil & Environm Engn Waterloo ON N2L 3G1 Canada

Seismic vulnerability evaluation of existing buildings is essential to minimize the destructive impacts of earthquakes. rapid visual screening (RVS) methods are simple and effective vulnerability assessment techniques to help quickly identify high-risk buildings for more detailed evaluations. Among various RVS methods, the Hassan-Sozen priority index (PI) is one of the simplest methods that can be used for low-rise reinforced concrete (RC) buildings. The PI relates simple, easily attainable geometric features of a building including number of stories, floor area, column area, and wall area to damageability. However, the relationship is overly simplified, and there is no absolute basis for defining damage classification boundaries that can be used to interpret the PI. Furthermore, given the lack of seismic parameters as inputs, the PI only allows for a relative evaluation of buildings in a specific region. To address these issues and develop a more broadly applicable RVS method, this study first proposes an improved PI evaluation method using machine learning techniques to define damage classification boundaries. Then, a new generalized RVS method is proposed that considers the PI input features and earthquake intensity measures to predict damage states. Data from six post-earthquake damage surveys (Duzce (1999), Bingol (2003), Nepal (2015), Taiwan (2016), Ecuador (2016), and Pohang (2017)) are used to train and evaluate the classification models. Two earthquake intensity features, modified Mercalli intensity and peak ground acceleration, are introduced to develop a new earthquake intensity aware RVS. The results of the proposed methodologies show a considerable improvement from the original PI with no judgment needed to define the damage classification boundaries.

关键词： Seismic vulnerability assessment rapid visual screening reinforced concrete priority index machine learning

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Seismic safety assessment of buildings and perceptions of earthquake risk among communities in Mingora, Swat, Pakistan

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2025年第4期10卷 1-18页

作者： Raza, Muhammad Umair Butt, Faheem Ahmad, Farhan Waqas, Rana Muhammad Univ Engn & Technol Dept Civil Engn Taxila 47050 Pakistan Western Sydney Univ Sch Engn Design & Built Environm Sydney NSW 2751 Australia

Pakistan has experienced several devastating earthquakes, including the 2005 Kashmir earthquake, which resulted in significant loss of life and damage. Mingora City, located in Seismic Zone 3, is particularly vulnerable and requires seismic evaluations and retrofitting of buildings to prevent future casualties and building collapses. Due to the high costs and complexity of seismic assessments, it is impractical to assess all buildings. This study used the FEMA P-154 rapid visual screening (RVS) method to assess 210 buildings. The study also examined local residents' risk perceptions through interviews. Findings revealed that most buildings had plan and vertical irregularities, such as short columns or soft storeys, and over 90% of buildings fall within damage grades 4 and 5, highlighting the urgent need for retrofitting. Poor construction quality and lack of maintenance were also noted, with 37% of buildings showing damage. Public awareness of earthquake safety was low, and 89.5% of residents agreed on the need for better education. The study highlights the need for public awareness campaigns on earthquake risks and safety.

关键词： Seismic vulnerability rapid visual screening Risk reduction Earthquake risk perception Mingora City

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Multihazard risk assessment of educational institutes of Dehradun, Uttarakhand

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INTERNATIONAL JOURNAL OF DISASTER RESILIENCE IN THE BUILT ENVIRONMENT 2024年第1期15卷 45-58页

作者： Chouhan, Shivani Narang, Aishwarya Mukherjee, Mahua Indian Inst Technol Roorkee Ctr Excellence Disaster Mitigat & Management Roorkee Uttar Pradesh India

Purpose In the event of a disaster, educational institutions like schools serve as lifeline buildings. Hence, it is crucial to safeguard these buildings for the communities that may depend on the school as a disaster shelter and aid center. Thus, this paper aims to conduct a multihazard risk assessment survey at 50 schools (with 246 building blocks) in Dehradun. Design methodology approach The past few decades have witnessed the impact of multihazard frequency in Uttarakhand, India, due to the geographical features of the Himalayas and its neo-tectonic mountain-building process. Dehradun is the capital of Uttarakhand state and comes under seismic zone IV, which is highly prone to earthquakes. Findings The hazard assessment is divided into two types of surveys: first, building-level surveys that include rapid visual screening, nonstructural risk assessment and fire safety audit, and second, campus-level surveys that include vulnerability analysis for earthquake, flood, industrial hazard, landslide and wind. Social implications This paper will list several gaps and unrecognized practices in the region that increase the schools' multihazard risk. The study's outcome will help prioritize the planning of disaster awareness, retrofitting execution, future construction practices and decision-making to minimize the risk and prepare the school for the upcoming disasters. Originality value Physical data were collected by the author to determine the multihazard risk analysis in 50 schools in the Dehradun District of Uttarakhand, India. The building- and campus-level surveys have been used to generate a database for the retrofit and renovation process for each individual school to use their budget fruitfully and in a planned way. The survey conducted is more effort and a more detailed risk evaluation which necessitates effectively mitigating and ensuring the potential safety of the region's schools.

关键词： Multihazard Risk assessment Vulnerability survey rapid visual screening School safety

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Seismic Vulnerability Assessment of Non-Structural Components-Methodology, Implementation Approach and Impact Assessment in South and Central Asia

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JOURNAL OF EARTHQUAKE ENGINEERING 2022年第3期26卷 1300-1324页

作者： Kumar, Amit Lang, Dominik H. Ziar, Hasibullah Singh, Yogendra Aga Khan Agcy Habitat Head Off Planning & Bldg Dept Dushanbe Tajikistan Norwegian Geotech Inst Nat Hazards Market Area Oslo Norway NORSAR Kjeller Norway Aga Khan Agcy Habitat Planning & Bldg Dept Kabul Afghanistan IIT Roorkee Dept Earthquake Engn Roorkee Uttar Pradesh India

Most frequently, the South and Central Asia region witnesses small to moderate magnitude earthquakes. In the past, this situation has resulted in massive property damage and losses apart from unaccounted operational dysfunction and human injuries and losses. There is a dire need to understand the underlying factors for these losses and prepare for the continuity of facility operations with loss reduction. To understand these requirements, Aga Khan Development Network (AKDN) carried out a Disaster Risk Management Initiative programme (now part of Aga Khan Agency for Habitat) across South and Central Asia between 2011 and 2016. One of the key outcomes of the initiative was to develop a non-structural hazard mitigation methodology, which should facilitate the assessment of all AKDN facilities for their safety and continuity of operations. The present manuscript intends to discuss the systematic approach in conducting rapid visual screening Surveys following the AKDN methodology to generate a priority list of structures considered for mitigation planning. In doing so, the manuscript puts focus on the detailed non-structural vulnerability assessment, which results in incremental non-structural hazard mitigation including housekeeping, bracing and anchoring Mechanical, Electrical and Plumbing (MEP) and Fixtures, Furniture and Equipment (FFEs), as well as architectural retrofitting. Various challenges are encountered during the mitigation plan implementation. So far, the developed methodology is tested on more than 5,500 individual structures. The criteria for effective mitigation implementation include safety, aesthetics, time, economy, and functionality. Another dimension of this manuscript is to discuss about implementation monitoring and how it has reduced the risk.

关键词： Seismic risk rapid visual screening detailed non-structural vulnerability assessment South and Central Asia

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Seismic Response of Masonry Buildings in Historical Centres Struck by the 2016 Central Italy Earthquake. Impact of Building Features on Damage Evaluation

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INTERNATIONAL JOURNAL OF ARCHITECTURAL HERITAGE 2022年第12期16卷 1859-1884页

作者： Valluzzi, Maria Rosa Sbrogio, Luca Saretta, Ylenia Wenliuhan, Heisha Univ Padua DBC Dept Cultural Heritage Padua Italy Guangzhou Univ EERTC Earthquake Engn Res & Test Ctr CIPAR China Italy Int Res Ctr Protect Hist Archit Guangzhou Peoples R China

Masonry buildings in historic centres are prone to seismic damage, due to either construction features or effects of changes over the time. Systematic studies on built heritage need to be updated, aimed at providing large number of data to identify recursive aspects affecting vulnerability and damage. In this study, over 2300 case studies (i.e., structural units/buildings) belonging to 20 villages struck by the 2016 Central Italy earthquake were analysed by a multi-level survey procedure recently upgraded at the University of Padova. The effect of damage was examined with respect to the local macroseismic intensity and computed by comparing the influence of features such as: settlements configuration, buildings aggregation, geometrical and architectural details, horizontal and vertical structural components, and constituent materials. This study lays the groundwork for new approaches to the analysis of construction parameters of existing masonry buildings, in view of their typological categorization and validation on a statistical base.

关键词： Historic centre macroseismic intensity masonry building onsite investigation rapid visual screening seismic damage

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ML-EHSAPP: a prototype for machine learning-based earthquake hazard safety assessment of structures by using a smartphone app

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EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING 2022年第11期26卷 5279-5299页

作者： Harirchian, Ehsan Jadhav, Kirti Kumari, Vandana Lahmer, Tom Bauhaus Univ Weimar Inst Struct Mech ISM D-99423 Weimar Germany

The recent devastating earthquakes have caused severe physical, social, and financial damage worldwide and indicate that many existing buildings, especially in developing countries, are not designed to withstand seismic hazards and resulting vulnerabilities to earthquakes. It is difficult, time-consuming, and costly to investigate and inspect all the existing buildings in detail before an earthquake, especially in urban areas. Therefore, rapid methods for evaluating the vulnerability of buildings have attracted the interest of researchers. This paper investigates the seismic susceptibility through the combination of buildings' geometrical attributes that affect the vulnerability of buildings and further extended into having foresight into the damage state of reinforced concrete (RC) buildings using artificial neural network (ANN). For this purpose, a multi-layer perceptron (MLP) network has been trained and optimized using two different databases of damaged buildings from the Nepal and Ecuador earthquakes. The results show the practicability and efficacy of the selected ANN approach for classifying actual damage grade based on structural damage, which the workflow can be followed and applied into other data from other regions for the detection of highly vulnerable buildings. A prototype of a smartphone app and its implementation was later introduced for data collection and vulnerability assessment purposes based on the proposed method.

关键词： Artificial neural network earthquake safety assessment machine learning vulnerability assessment rapid visual screening reinforced concrete buildings smartphone

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