Seismic hazard in Mexico City governed by site effects. The M8.1 1985 subduction zone earthquake, which caused significant damage and loss of thousands of lives at 350 km epicentral distance, has become the quintessential example of the role that site effects can play in modifying the amplitude, frequency, and duration of ground shaking; and in aggravating the catastrophic consequences of earthquakes. We here present observations and analyses of the M7.1 September 19, 2017, event that—while triggered by an intraplate rupture at approximately half the epicentral distance of the 1985 event relative to Mexico City—caused severe structural damage to a few tens of buildings located in a relatively narrow zone between the hill and lake zones of the basin, known as the transition zone. We show that the M 7.1 mainshock exposed the vulnerabilities of the pre-1985 building code in the transition zone; but more importantly highlighted the improvement of the 1987 building code revision in terms of the performance of modern high-rise buildings that suffered catastrophic consequences during the 1985 Michoácan earthquake sequence. We next analyze several records collected at stations in the basin over the past 20 years. We highlight the importance of three-dimensional heterogeneity of the basin sediments, the coupling between hydrological setting and site response and their evolution with time, and the energy interaction between the deep basin edge and the shallow clay layers. Results presented are the collective effort of the GEER teams that were deployed to perform post-earthquake reconnaissance in the affected regions of the epicentral area and in Mexico City after the M 7.1 September 19, 2017, earthquake sequence.
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Guided post-earthquake reconnaissance surveys considering resource constraints for regional damage inference
The extent of loss in a seismic hazard can be moderated with on-time allocation of funds and initiation of recovery tasks. Among various examinations conducted following the hazard, buildings damages are assessed as part of the reconnaissance survey to learn and document the impact of the earthquake on structures. The results of the survey are used in financial aid estimation, which is crucial for the community rapid recovery acts after the hazard. Due to the urgent need for this information, the amount of information gained per unit of time should be optimized. This article aims at answering the question of how to maximize the information gain in the presence of resource constraints by directing the efforts of a reconnaissance surveying team. A data-driven method is proposed that actively learns the patterns of damage and recommends the most informative buildings to be inspected while considering the resource limitations. The framework utilizes an efficient active learning method based on mutual information and developed for Gaussian process regression (GPR) to identify the information-rich cases. To assess the contribution of information gain and resource allocation in the overall outcome of the damage inference, two simulated earthquake testbeds are studied. It is shown that in a co-optimization approach, damage labels of the majority of buildings can be accurately predicted after 1 week of damage inspections.
more » « less- NSF-PAR ID:
- 10373083
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Earthquake Spectra
- Volume:
- 38
- Issue:
- 4
- ISSN:
- 8755-2930
- Page Range / eLocation ID:
- p. 2813-2834
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Post‐earthquake reconnaissance survey of structural damage is an effective way of documenting and understanding the impact of earthquakes on structures. This article aims at providing an efficient data‐based framework that reduces the required time for reconnaissance missions and predicts the damage intensities for every building in the affected region. We hypothesize that a joint selection of necessary structural and earthquake parameters along with sparse damage observations are sufficient to train a supervised learning algorithm and accurately infer the damage for other buildings in the region. Gaussian process regression is employed to prove the hypothesis for probabilistic inference of different damage indices. The algorithm performs efficiently by selecting a set of diverse and representative buildings for damage observations using K‐medoids clustering. To validate the hypothesis and the proposed method, the algorithm framework is implemented on two severe earthquake simulation testbeds. The impacts of different building and ground motion variables on the damage inference performance are discussed. Furthermore, the effectiveness of observation sampling by clustering in the post‐earthquake damage inference is compared with random sampling.
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null (Ed.)In the aftermath of earthquake events, reconnaissance teams are deployed to gather vast amounts of images, moving quickly to capture perishable data to document the performance of infrastructure before they are destroyed. Learning from such data enables engineers to gain new knowledge about the real-world performance of structures. This new knowledge, extracted from such visual data, is critical to mitigate the risks (e.g., damage and loss of life) associated with our built environment in future events. Currently, this learning process is entirely manual, requiring considerable time and expense. Thus, unfortunately, only a tiny portion of these images are shared, curated, and actually utilized. The power of computers and artificial intelligence enables a new approach to organize and catalog such visual data with minimal manual effort. Here we discuss the development and deployment of an organizational system to automate the analysis of large volumes of post-disaster visual data, images. Our application, named the Automated Reconnaissance Image Organizer (ARIO), allows a field engineer to rapidly and automatically categorize their reconnaissance images. ARIO exploits deep convolutional neural networks and trained classifiers, and yields a structured report combined with useful metadata. Classifiers are trained using our ground-truth visual database that includes over 140,000 images from past earthquake reconnaissance missions to study post-disaster buildings in the field. Here we discuss the novel deployment of the ARIO application within a cloud-based system that we named VISER (Visual Structural Expertise Replicator), a comprehensive cloud-based visual data analytics system with a novel Netflix-inspired technical search capability. Field engineers can exploit this research and our application to search an image repository for visual content. We anticipate that these tools will empower engineers to more rapidly learn new lessons from earthquakes using reconnaissance data.more » « less
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Post-disaster reconnaissance is vital for assessing the impact of a natural disaster on the built environment and informing improvements in design, construction, risk mitigation, and our understanding of extreme events. The data obtained from reconnaissance can also be utilized to improve disaster recovery planning by maximizing resource efficiency, minimizing waste, and promoting resilience in future disasters. This paper aims to investigate existing reconnaissance reports and datasets to identify the factors that impact the reusability of buildings post-disaster and to recommend strategies that align with circular economy goals. The study adopted a three-step research methodology to attain the proposed goals: (1) thematic analysis was used to evaluate types of damages reported in the reconnaissance reports; (2) a supervised machine-learning algorithm was employed to analyze reconnaissance datasets; and (3) a concept map was developed based on interviews of 109 stakeholders in disaster-prone communities to recommend strategies to adopt circular economy practices post-disaster. The study results highlight the recurring risks of damage to different parts of the building and how circular economy resilience practices like deconstruction can minimize waste and maximize resource efficiency during post-disaster recovery. The findings of the study promote a more regenerative economy to build resilience to the challenges of future extreme weather events.
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null (Ed.)The 2019 Ridgecrest earthquake sequence produced a 4 July M 6.5 foreshock and a 5 July M 7.1 mainshock, along with 23 events with magnitudes greater than 4.5 in the 24 hr period following the mainshock. The epicenters of the two principal events were located in the Indian Wells Valley, northwest of Searles Valley near the towns of Ridgecrest, Trona, and Argus. We describe observed liquefaction manifestations including sand boils, fissures, and lateral spreading features, as well as proximate non‐ground failure zones that resulted from the sequence. Expanding upon results initially presented in a report of the Geotechnical Extreme Events Reconnaissance Association, we synthesize results of field mapping, aerial imagery, and inferences of ground deformations from Synthetic Aperture Radar‐based damage proxy maps (DPMs). We document incidents of liquefaction, settlement, and lateral spreading in the Naval Air Weapons Station China Lake US military base and compare locations of these observations to pre‐ and postevent mapping of liquefaction hazards. We describe liquefaction and ground‐failure features in Trona and Argus, which produced lateral deformations and impacts on several single‐story masonry and wood frame buildings. Detailed maps showing zones with and without ground failure are provided for these towns, along with mapped ground deformations along transects. Finally, we describe incidents of massive liquefaction with related ground failures and proximate areas of similar geologic origin without ground failure in the Searles Lakebed. Observations in this region are consistent with surface change predicted by the DPM. In the same region, geospatial liquefaction hazard maps are effective at identifying broad percentages of land with liquefaction‐related damage. We anticipate that data presented in this article will be useful for future liquefaction susceptibility, triggering, and consequence studies being undertaken as part of the Next Generation Liquefaction project.more » « less
