Yadong Xu¹, Weixing Hong², Mohammad Noori^3,6,*, Wael A. Altabey^4,*, Ahmed Silik⁵, Nabeel S.D. Farhan²

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 763-783, 2024, DOI:10.32604/sdhm.2024.053763

Abstract This study introduces an innovative “Big Model” strategy to enhance Bridge Structural Health Monitoring (SHM) using a Convolutional Neural Network (CNN), time-frequency analysis, and fine element analysis. Leveraging ensemble methods, collaborative learning, and distributed computing, the approach effectively manages the complexity and scale of large-scale bridge data. The CNN employs transfer learning, fine-tuning, and continuous monitoring to optimize models for adaptive and accurate structural health assessments, focusing on extracting meaningful features through time-frequency analysis. By integrating Finite Element Analysis, time-frequency analysis, and CNNs, the strategy provides a comprehensive understanding of bridge health. Utilizing diverse sensor More >

V. Mallardo^1,2, M.H. Aliabadi³

Structural Durability & Health Monitoring, Vol.9, No.4, pp. 287-323, 2013, DOI:10.32604/sdhm.2013.009.287

Abstract The optimum location of the sensors is a critical issue of any successful Structural Health Monitoring System. Sensor optimization problems encompass mainly three areas of interest: system identification, damage identification and impact identification. The current paper is intended as a review of the state of the art at the year 2012 and going back to 1990. The above topics have been dealt with in separate contexts so far but they contain interesting common elements to be exploited. More >

S.Mohanty¹, A. Chattopadhyay², J. Wei³, P. Peralta⁴

Structural Durability & Health Monitoring, Vol.5, No.3, pp. 227-250, 2009, DOI:10.3970/sdhm.2009.005.227

Abstract This paper proposes unsupervised system identification based methods to estimate time-series fatigue damage states in real-time. Ultrasound broadband input is used for active damage interrogation. Novel damage index estimation techniques based on dual sensor signals are proposed. The dual sensor configuration is used to remove electrical noise, as well as to improve spatial resolution in damage state estimation. The scalar damage index at any particular damage condition is evaluated using nonparametric system identification techniques, which includes an empirical transfer function estimation approach and a correlation analysis approach. In addition, the effectiveness of two sensor configurations More >

S.Mohanty¹, A. Chattopadhyay², J. Wei³, P. Peralta⁴

Structural Durability & Health Monitoring, Vol.5, No.1, pp. 33-56, 2009, DOI:10.3970/sdhm.2009.005.033

Abstract The current state of the art in the area of real time structural health monitoring techniques offers adaptive damage state prediction and residual useful life assessment. The present paper discusses the use of an integrated prognosis model, which combines an on-line state estimation model with an off-line predictive model to adaptively estimate the residual useful life of an Al-6061 cruciform specimen under biaxial loading. The overall fatigue process is assumed to be a slow time scale process compared to the time scale at which, the sensor signals were acquired for on-line state estimation. The on-line… More >