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RISDevelopment of Fragility Functions for Brick Masonry Structures Using Damage Data from September 24, 2019, Earthquake in Mirpur, Azad Kashmir
Publisher: ASCE
Abstract: This paper presents fragility functions for clay brick unreinforced masonry (URM) buildings that were developed using the data of damage and earthquake intensity by the September 24, 2019, Mirpur, Azad Jammu and Kashmir ...
Intelligent Multitasking Framework for Boundary-Preserving Semantic Segmentation, Width Estimation, and Propagation Modeling of Concrete Cracks
Publisher: American Society of Civil Engineers
Abstract: Crack detection is crucial for ensuring the durability, safety, and structural integrity of civil infrastructure. Traditionally, this task involves manual inspections and crack width measurements using a crack width ...
Vector-Quantized Variational Teacher and Multimodal Collaborative Student for Crack Segmentation via Knowledge Distillation
Publisher: American Society of Civil Engineers
Abstract: This paper proposes a novel method for real-time crack segmentation in infrastructure inspection that achieves state-of-the-art performance. This approach leverages knowledge distillation, in which a vector-quantized ...
Multimodal Fusion Network for Crack Segmentation with Modified U-Net and Transfer Learning–Based MobileNetV2
Publisher: American Society of Civil Engineers
Abstract: This study introduces a state-of-the-art methodology for addressing crack segmentation challenges in structure health monitoring, a crucial concern in infrastructure maintenance. The main objective is to enhance real-time ...
Investigation of Damage to Reinforced Concrete Buildings Due to the 2019 Mirpur Earthquake, Azad Kashmir
Publisher: ASCE
Abstract: This paper presents the results of damage surveys in Mirpur, in the Azad Jammu and Kashmir region of Pakistan, which was affected by a moderate earthquake on September 24, 2019. The epicenter of this earthquake was near ...
LiDAR-Simulated Multimodal and Self-Supervised Contrastive Digital Twin Approach for Probabilistic Point Cloud Generation of Rail Fasteners
Publisher: American Society of Civil Engineers
Abstract: This study presents a novel deep-learning framework designed to efficiently generate high-fidelity three-dimensional (3D) point clouds of rail fasteners. The proposed method overcomes limitations associated with traditional ...