Khalid M. Mosalam

Yuqing Gao, Pengyuan Zhai, Khalid M. Mosalam

In recent years, applying deep learning (DL) to assess structural damages has gained growing popularity in vision-based structural health monitoring (SHM). However, both data deficiency and class-imbalance hinder the wide adoption of DL in practical applications of SHM. Common mitigation strategies include transfer learning, over-sampling, and under-sampling, yet these ad-hoc methods only provide limited performance boost that varies from one case to another. In this work, we introduce one variant of the Generative Adversarial Network (GAN), named the balanced semi-supervised GAN (BSS-GAN). It adopts the semi-supervised learning concept and applies balanced-batch sampling in training to resolve low-data and imbalanced-class problems. A series of computer experiments on concrete cracking and spalling classification were conducted under the low-data imbalanced-class regime with limited computing power. The results show that the BSS-GAN is able to achieve better damage detection in terms of recall and $F_β$ score than other conventional methods, indicating its state-of-the-art performance.

Yuqing Gao, Guanren Zhou, Khalid M. Mosalam

Artificial Intelligence (AI)-aided vision-based Structural Health Monitoring (SHM) has emerged as an effective approach for monitoring and assessing structural condition by analyzing image and video data. By integrating Computer Vision (CV) and Deep Learning (DL), vision-based SHM can automatically identify and localize visual patterns associated with structural damage. However, previous works typically generate only discrete outputs, such as damage class labels and damage region coordinates, requiring engineers to further reorganize and analyze these results for evaluation and decision-making. In late 2022, Large Language Models (LLMs) became popular across multiple fields, providing new insights into AI-aided vision-based SHM. In this study, a novel LLM-based Disaster Reconnaissance Summarization (LLM-DRS) framework is proposed. It introduces a standard reconnaissance plan in which the collection of vision data and corresponding metadata follows a well-designed on-site investigation process. Text-based metadata and image-based vision data are then processed and integrated into a unified format, where well-trained Deep Convolutional Neural Networks extract key attributes, including damage state, material type, and damage level. Finally, all data are fed into an LLM with carefully designed prompts, enabling the LLM-DRS to generate summary reports for individual structures or affected regions based on aggregated attributes and metadata. Results show that integrating LLMs into vision-based SHM, particularly for rapid post-disaster reconnaissance, demonstrates promising potential for improving resilience of the built environment through effective reconnaissance.

Yuan Xinjie, Khalid M. Mosalam

Fire safety is crucial for ensuring the stability of building structures, yet evaluating whether a structure meets fire safety requirement is challenging. Fires can originate at any point within a structure, and simulating every potential fire scenario is both expensive and time-consuming. To address this challenge, we propose the concept of the Most Fire-Sensitive Point (MFSP) and an efficient machine learning framework for its identification. The MFSP is defined as the location at which a fire, if initiated, would cause the most severe detrimental impact on the building's stability, effectively representing the worst-case fire scenario. In our framework, a Graph Neural Network (GNN) serves as an efficient and differentiable agent for conventional Finite Element Analysis (FEA) simulators by predicting the Maximum Interstory Drift Ratio (MIDR) under fire, which then guides the training and evaluation of the MFSP predictor. Additionally, we enhance our framework with a novel edge update mechanism and a transfer learning-based training scheme. Evaluations on a large-scale simulation dataset demonstrate the good performance of the proposed framework in identifying the MFSP, offering a transformative tool for optimizing fire safety assessments in structural design. All developed datasets and codes are open-sourced online.

Alicia Y. Tsai, Selim Gunay, Minjune Hwang et al.

Post-hazard reconnaissance for natural disasters (e.g., earthquakes) is important for understanding the performance of the built environment, speeding up the recovery, enhancing resilience and making informed decisions related to current and future hazards. Natural language processing (NLP) is used in this study for the purposes of increasing the accuracy and efficiency of natural hazard reconnaissance through automation. The study particularly focuses on (1) automated data (news and social media) collection hosted by the Pacific Earthquake Engineering Research (PEER) Center server, (2) automatic generation of reconnaissance reports, and (3) use of social media to extract post-hazard information such as the recovery time. Obtained results are encouraging for further development and wider usage of various NLP methods in natural hazard reconnaissance.