skip to main content


Search for: All records

Award ID contains: 2131111

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    In high seismic risk regions, it is important for city managers and decision makers to create programs to mitigate the risk for buildings. For large cities and regions, a mitigation program relies on accurate information of building stocks, that is, a database of all buildings in the area and their potential structural defects, making them vulnerable to strong ground shaking. Structural defects and vulnerabilities could manifest via the building's appearance. One such example is the soft‐story building—its vertical irregularity is often observable from the facade. This structural type can lead to severe damage or even collapse during moderate or severe earthquakes. Therefore, it is critical to screen large building stock to find these buildings and retrofit them. However, it is usually time‐consuming to screen soft‐story structures by conventional methods. To tackle this issue, we used full image classification to screen them out from street view images in our previous study. However, full image classification has difficulties locating buildings in an image, which leads to unreliable predictions. In this paper, we developed an automated pipeline in which we segment street view images to identify soft‐story buildings. However, annotated data for this purpose is scarce. To tackle this issue, we compiled a dataset of street view images and present a strategy for annotating these images in a semi‐automatic way. The annotated dataset is then used to train an instance segmentation model that can be used to detect all soft‐story buildings from unseen images.

     
    more » « less
  2. Abstract

    Nonlinear response history analysis (NLRHA) is generally considered to be a reliable and robust method to assess the seismic performance of buildings under strong ground motions. While NLRHA is fairly straightforward to evaluate individual structures for a select set of ground motions at a specific building site, it becomes less practical for performing large numbers of analyses to evaluate either (1) multiple models of alternative design realizations with a site‐specific set of ground motions, or (2) individual archetype building models at multiple sites with multiple sets of ground motions. In this regard, surrogate models offer an alternative to running repeated NLRHAs for variable design realizations or ground motions. In this paper, a recently developed surrogate modeling technique, called probabilistic learning on manifolds (PLoM), is presented to estimate structural seismic response. Essentially, the PLoM method provides an efficient stochastic model to develop mappings between random variables, which can then be used to efficiently estimate the structural responses for systems with variations in design/modeling parameters or ground motion characteristics. The PLoM algorithm is introduced and then used in two case studies of 12‐story buildings for estimating probability distributions of structural responses. The first example focuses on the mapping between variable design parameters of a multidegree‐of‐freedom analysis model and its peak story drift and acceleration responses. The second example applies the PLoM technique to estimate structural responses for variations in site‐specific ground motion characteristics. In both examples, training data sets are generated for orthogonal input parameter grids, and test data sets are developed for input parameters with prescribed statistical distributions. Validation studies are performed to examine the accuracy and efficiency of the PLoM models. Overall, both examples show good agreement between the PLoM model estimates and verification data sets. Moreover, in contrast to other common surrogate modeling techniques, the PLoM model is able to preserve correlation structure between peak responses. Parametric studies are conducted to understand the influence of different PLoM tuning parameters on its prediction accuracy.

     
    more » « less
  3. The detailed evaluation of expected losses and damage experienced by structural and nonstructural components is a fundamental part of performance-based seismic design and assessment. The FEMA P-58 methodology represents the state of the art in this area. Increasing interest in improving structural performance and community resilience has led to widespread adoption of this methodology and the library of component models published with it. This study focuses on the modeling of economies of scale for repair cost calculation and specifically highlights the lack of a definition for aggregate damage, a quantity with considerable influence on the component repair costs. The article illustrates the highly variable and often substantial impact of damage aggregation that can alter total repair costs by more than 25%. Four so-called edge cases representing different damage aggregation methods are introduced to investigate which components experience large differences in their repair costs and under what circumstances. A three-step evaluation strategy is proposed that allows engineers to quickly evaluate the potential impact of damage aggregation on a specific performance assessment. This helps users of currently available assessment tools to recognize and communicate this uncertainty even when the tools they use only support one particular damage aggregation method. A case study of a 9-story building illustrates the proposed strategy and the impact of this ambiguity on the performance of a realistic structure. The article concludes with concrete recommendations toward the development of a more sophisticated model for repair consequence calculation.

     
    more » « less
    Free, publicly-accessible full text available February 12, 2025
  4. Existing building recognition methods, exemplified by BRAILS, utilize supervised learning to extract information from satellite and street-view images for classification and segmentation. However, each task module requires human-annotated data, hindering the scalability and robustness to regional variations and annotation imbalances. In response, we propose a new zero-shot workflow for building attribute extraction that utilizes large-scale vision and language models to mitigate reliance on external annotations. The proposed workflow contains two key components: image-level captioning and segment-level captioning for the building images based on the vocabularies pertinent to structural and civil engineering. These two components generate descriptive captions by computing feature representations of the image and the vocabularies, and facilitating a semantic match between the visual and textual representations. Consequently, our framework offers a promising avenue to enhance AI-driven captioning for building attribute extraction in the structural and civil engineering domains, ultimately reducing reliance on human annotations while bolstering performance and adaptability. 
    more » « less
  5. The NHERI SimCenter is a nine-year research project that aims to advance the simulation of natural hazard impact on the built environment and communities. The SimCenter is developing several open-source workflow applications and an underlying scientific application framework. All applications built on this framework provide an OpenSees interface that enables users to use their existing models in advanced simulation studies, such as local and regional performance assessment, and uncertainty quantification (UQ). SimCenter applications provide researchers an opportunity to explore different extensions of their models by lowering the interdisciplinary barrier and encouraging collaboration. Among the applications, quoFEM provides access to UQ analyses with an easy-to-use, standardized interface. This work demonstrates the research enabled by quoFEM through the example of model calibration using PM4Sand, a soil constitutive model available in OpenSees. After an initial sensitivity analysis, the model is calibrated using Bayesian inference based on observations of hysteretic soil response from cyclic direct simple shear tests. The uncertainty in the model parameters is used in forward propagation to explore plausible lateral spreading scenarios due to seismic liquefaction. The results demonstrate the utility of quoFEM to the OpenSees community as a UQ-enabling tool. 
    more » « less