A Data Driven Framework For Regional Assessment Of Seismically Vulnerable Buildings

The urban region's seismic resilience is being actively studied in recent years as a measure for risk mitigation, where the identification of seismically vulnerable buildings and the assessment of their performance play indispensable roles. However, it is a labor-intensive and computationally expensive task to evaluate tens of thousands of buildings in a region because the identification requires professional judgment at a site and the seismic assessment demands comprehensive modeling depending on structure-specific data. Nevertheless, it is feasible with the aid of advanced development of the Internet of Things (IoT) and computer technology. In this study, a data-driven framework including two pipelines that focus on soft-story buildings and non-ductile reinforced concrete frames is proposed. The first pipeline focuses on identifying soft-story buildings in the city of Santa Monica (California) through 3D point clouds and convolutional neural networks (CNNs). Although prior studies showed promising results in detecting soft-story buildings based on well-selected street-view images, false predictions are common when it is applied to real-world data. To address this issue, the pipeline implements point-cloud data where spatial information is available to segment building points and extract density features for training deep learning models and identifying soft-story buildings. The transfer learning (TL) technique is adopted to avoid overfitting in deep neural networks, and the parameters within the pipeline are investigated for optimal performance. The results illustrate the potential applicability of the pipeline for developing pre-and post-event countermeasures. The second pipeline focuses on another seismically vulnerable building, namely, the non-ductile reinforced concrete building (NDRCB). Prior studies indicated around 1,500 NDRCBs in Los Angeles that are urgently waiting for detailed assessment and mandatory retrofit or demolition if necessary. Because the fulfillment of these ordinances will last for decades, the potential risk of major losses will persist. To this end, an automatic method that harvests building information from IoT and imagery data generates archetypal models, conducts probabilistic seismic assessment, and estimates the losses for NDRCB frames is hence developed. The accuracy of the data harvesting module using deep CNNs is validated with the existing inventory data. The archetypal frames are developed based on the era-specific representative code and are validated through nonlinear static and nonlinear dynamic analyses of previously investigated NDRCBs. State-of-the-practice loss estimation methodologies including HAZUS and FEMA P-58 are adopted in the pipeline for constructing damage fragility functions and corresponding losses. The regional application focuses on intensity-based assessment for thousands of individual buildings instead of a scenario-based assessment. The outcomes of expected losses and repair/reconstruction time emphasize the vulnerability of NDRCBs in Los Angeles, and the presented pipeline is believed to bridge the gaps between property owners, engineers, and decision-makers. This research demonstrates how advanced data mining techniques and data-driven approaches can aid to solve civil engineering problems. While the framework currently focuses on soft-story and non-ductile frame buildings, it is expected to be extended in-depth and breadth in the future. That is, more detailed models and other seismically vulnerable infrastructures can be included.

Structural seismic resilience society has been grown rapidly in the past three decades. Extensive probabilistic techniques have been developed to address uncertainties from ground motions and building systems to reduce structural damage, economic loss and social impact of buildings subjected to seismic hazards where seismic structural responses are essential and often are retrieved through Nonlinear Response History Analysis. This process is largely limited by accuracy of model and computational effort. An alternative data-driven framework is proposed to reconstruct structure responses through machine learning techniques from limited available sources which may potentially benefit for "real-time" interpolating monitoring data to enable rapid damage assessment and reducing computational effort for regional seismic hazard assessment. It also provides statistical insight to understand uncertainties of seismic building responses from both structural and earthquake engineering perspective.

Empirical Seismic Vulnerability and Resilience Assessment of Building Clusters analyzes the seismic vulnerability analysis of four types of structures and studies and discusses the evaluation of structural damage using risk analysis and shaking table test methods. It does not consider the contribution of typical empirical structural seismic damage data to structural vulnerability assessment and prediction. It is recognized that the impact of earthquakes on large-scale areas is extensive, not only on a building but also on a group of buildings. The book is based on the research background of typical seismic damage characteristics of 11 types of engineering structures. It analyzes the characteristics of the vulnerability of various structures and provides measures and methods to improve the vulnerability of various structures. Combined with probability risk, reliability, machine learning, and other means, vulnerability prediction and evaluation models are established, respectively, and the rationality of the models is verified by hundreds of onsite earthquake damage survey data. Increases engineers' theoretical and practical knowledge of field investigation to help improve their efficiency and the quality of future work Includes the analysis of hundreds of earthquake field survey data Provides a vulnerability assessment of diversified structural experience

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 the occurrence of an earthquake 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 which is the topic of inquiry. In the end, this helps 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 is 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 is of significant assistance to the authorities that are accountable for the restoration of the preexisting buildings and the cultural heritages.

The Rapid Visual Screening (RVS) handbook can be used by trained personnel to identify, inventory, and screen buildings that are potentially seismically vulnerable. The RVS procedure comprises a method and several forms that help users to quickly identify, inventory, and score buildings according to their risk of collapse if hit by major earthquakes. The RVS handbook describes how to identify the structural type and key weakness characteristics, how to complete the screening forms, and how to manage a successful RVS program.

Data Science and Engineering Volume 9: Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics, 2021, the ninth volume of nine from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on: Data Science in Engineering Applications Engineering Mathematics Computational Methods in Engineering

Seismic Vulnerability Assessment of Civil Engineering Structures at Multiple Scales: From Single Buildings to Large-Scale Assessment provides an integrated, multiscale platform for fundamental and applied studies on the seismic vulnerability assessment of civil engineering structures, including buildings with different materials and building typologies. The book shows how various outputs obtained from different scales and layers of assessment (from building scale to the urban area) can be used to outline and implement effective risk mitigation, response and recovery strategies. In addition, it highlights how significant advances in earthquake engineering research have been achieved with the rise of new technologies and techniques. The wide variety of construction and structural systems associated with the complex behavior of their materials significantly limits the application of current codes and building standards to the existing building stock, hence this book is a welcomed guide on new construction standards and practices. Provides the theoretical backgrounds on the most advanced seismic vulnerability assessment approaches at different scales and for most common building typologies Covers the most common building typologies and the materials they are made from, such as concrete, masonry, steel, timber and raw earth Presents practical guidelines on how the outputs coming from such approaches can be used to outline effective risk mitigation and emergency planning strategies

Rehabilitation of heritage monuments provides sustainable development and cultural significance to a region. The most sensitive aspect of the refurbishment of existing buildings lies in the renovation and recovery of structural integrity and public safety. The Handbook of Research on Seismic Assessment and Rehabilitation of Historic Structures evaluates developing contributions in the field of earthquake engineering with regards to the analysis and treatment of structural damage inflicted by seismic activity. This book is a vital reference source for professionals, researchers, students, and engineers active in the field of earthquake engineering who are interested in the emergent developments and research available in the preservation and rehabilitation of heritage buildings following seismic activity.