Damage detection of nonlinear structures using probability density ratio estimation

Zero‐shot learning, applied with vision‐language pretrained (VLP) models, is expected to be an alternative to existing deep learning models for defect detection, under insufficient dataset. However, VLP models, including contrastive language‐image pretraining (CLIP), showed fluctuated performance on prompts (inputs), resulting in research on prompt engineering—optimization of prompts for improving performance. Therefore, this study aims to identify the features of a prompt that can yield the best performance in classifying and detecting building defects using the zero‐shot and few‐shot capabilities of CLIP. The results reveal the following: (1) domain‐specific definitions are better than general definitions and images; (2) a complete sentence is better than a set of core terms; and (3) multimodal information is better than single‐modal information. The resulting detection performance using the proposed prompting method outperformed that of existing supervised models.

Section: Proposed Prompt Engineering Methodsmentioning

confidence: 99%

Prompt engineering for zero‐shot and few‐shot defect detection and classification using a visual‐language pretrained model

Yong

Jeon

Gil

et al. 2022

“…The method requires acceleration measurements recorded directly from the beam, and damage was simulated during testing by using a lumped mass attached at different locations. Yulong Zhang et al (2022) also carried out laboratory testing of a beam, where a lumped mass was used to simulate the effects of damage. Their approach used acceleration measurements recorded from the beam to identify damage from the nonlinear structural response.…”

Section: Traditional Bridge Monitoring Approachesmentioning

confidence: 99%

A deep‐learning framework for classifying the type, location, and severity of bridge damage using drive‐by measurements

Corbally,

Malekjafarian

2023

This paper proposes a new deep‐learning framework for drive‐by bridge condition monitoring. The proposed approach represents a bridge monitoring regime that enables the presence, type, location, and severity of bridge damage to be identified purely from measurements taken on a passing vehicle, without needing any pre‐measured training data. The computational framework adopts a numerical vehicle–bridge interaction (VBI) model, which is automatically calibrated using only the vehicle vibration measurements. The calibrated model is used to generate labeled training data, eliminating the practical difficulties associated with collecting training data for damaged bridge scenarios. The numerically simulated data are used to train a convolutional neural network that can then classify the actual damage characteristics of a bridge. The method is tested using a laboratory‐scale VBI model, and results show that the algorithm can accurately identify the presence of seized bearings and cracking in the bridge beams. The algorithm shows good accuracy when identifying the type and extent of damage. The location of bearing damage can generally be identified; however, the location of cracking is less accurate at lower damage levels. The proposed framework represents a significant improvement on existing techniques for indirect bridge monitoring.

“…Damage detection methods of concrete structures have been successively proposed in recent years. For example, a damage detection method was proposed based on an initially nonlinear system (Yulong Zhang et al., 2022). Comparing the probability density function of the undamaged and potentially damaged states, the reported method detected damages by measuring the difference using the Kullback–Leibler divergence.…”

Section: Introductionmentioning

confidence: 99%

Vision‐based real‐time marine and offshore structural health monitoring system using underwater robots

Jiao

Zhang

et al. 2023

Structural health monitoring (SHM) plays an increasingly vital role in guaranteeing the operation success of marine and offshore (MO) structures while reducing their risks of structural failure. In this study, a real‐time MO‐SHM system based on the highly controllable underwater robots is developed, which is functionalized with three modules, that is, underwater monitoring robots, vision‐based image processing and analyzing, and time‐dependent damage assessing and early warning. The robotic module is actuated by the hybrid driving method that combines the combustion‐based actuators (ejection) and propeller thrusters (propulsion) for transient actuation ability and well reliability in complex underwater environments. The image processing and analyzing module is conducted based on the You Only Look Once (YOLO)‐Underwater model that is expanded by the transfer learning and attention mechanisms and the underwater preconditioning and warning correction added for the specific underwater concrete identification. The damage assessing and early warning module is achieved by the time‐dependent hybrid analytic hierarchy process method that is integrated with the ordered weighted averaging and entropy weight method for more objective assessment and feedback. The reported MO‐SHM system provides design guidance for the next‐generation multifunctional underwater devices that combine analyzing and early warning for underwater concrete structures.