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RISDamage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid Data
Publisher: ASCE
Abstract: The success of detecting damage robustly relies on the availability of long periods of past data covering multiple weather scenarios and on the information contained in the data used during the learning process. Thus, the ...
Hybrid Approach for Supervised Machine Learning Algorithms to Identify Damage in Bridges
Publisher: American Society of Civil Engineers
Abstract: In bridges, monitoring data usually correspond to normal operational and environmental conditions, resulting in a lack of damage-related data. For this reason, machine learning algorithms for damage detection are typically ...
Use of Time-Series Predictive Models for Piezoelectric Active-Sensing in Structural Health Monitoring Applications
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: In this paper, time domain data from piezoelectric active-sensing techniques is utilized for structural health monitoring (SHM) applications. Piezoelectric transducers have been increasingly used ...
Finite Element–Based Machine-Learning Approach to Detect Damage in Bridges under Operational and Environmental Variations
Publisher: American Society of Civil Engineers
Abstract: In the last decades, the long-term structural health monitoring of civil structures has been mainly performed using two approaches: model based and data based. The former approach tries to identify damage by relating the ...