Thanh Q. Nguyen

Bio: Thanh Q. Nguyen is an academic researcher from Ho Chi Minh City University of Technology. The author has contributed to research in topics: Spectral density & Beam (structure). The author has an hindex of 6, co-authored 21 publications receiving 76 citations. Previous affiliations of Thanh Q. Nguyen include Hanoi University of Industry & Vietnam National University, Ho Chi Minh City.

A statistical approach for evaluating crack defects in structures under dynamic responses

Abstract: This article presents a new approach to processing data in identifying and evaluating defects of structures with deformation measurement signals. By using sensitive characteristics observed in the ...

Detecting and Evaluating Defects in Beams by Correlation Coefficients

Abstract: This research proposes a correlation coefficient for detecting and evaluating defects in beams, which brings about a positive outcome in terms of accuracy and efficiency. This parameter surpasses other parameters, such as natural frequency and damping coefficient, thanks to its sensitivity to structural changes. Our results show that although the damping coefficient had more variation than the natural frequency value in the same experiment, its changes were insufficient and unstable at different levels of defects. In addition, the proposed correlation coefficient parameter has a linear characteristic and always changes significantly according to increasing levels of defects. The results outweigh damping coefficient and natural frequency values. Furthermore, this value is always sensitive to measurement channels, which could be an important factor in locating defects in beams. The testing index is statistically evaluated by a normal distribution of the amplitude value of vibration measurement signals. Changes and shifts in this distribution are the basis for evaluating beam defects. Thus, the suggested parameter is a reliable alternative for assessing the defects of a structure.

An efficient approach for damage identification based on improved machine learning using PSO-SVM

Abstract: Structural health monitoring (SHM) and Non-destructive Damage Identification (NDI) using responses of structures under dynamic excitation have an imperative role in the engineering application to make the structures safe. Interpretations of structural responses known as inverse problems are emerging topics with a large body of works in the literature. They have been widely solved with Machine Learning (ML) techniques such as Artificial Neural Network (ANN), Deep Neural Network (DNN), Adaptive Network-based Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM). Nonetheless, these approaches can precisely predict the inverse problems of civil structures (e.g., truss or frame systems) with low damage levels, which have to wait until the structures reach certain damage or deteriorate level. The issue is related to the fact that most of the real structures have very low damage levels during their routine maintenances and usually be neglected due to limitations of the current techniques. This paper proposes a combination of Particle Swarm Optimization and Support Vector Machine (PSO-SVM) for damage identifications. The proposed approach is inspired by the effective searching capability of PSO, which can eliminate the redundant input parameters and robust SVM technique to classify damage locations effectively. In other words, natural frequencies and mode shapes extracted from the numerical examples of truss and frame structures are used as input parameters in which the redundant parameters might lead to reduction of the accuracy in the predicting models. The proposed PSO-SVM shows superior accuracy prediction in both damage locations and damage levels compared to the other ML models. It also substantially outperforms other ML models through validated cases of low damage levels.

Statistical analysis of online strain response and its application in fatigue assessment of a long-span steel bridge

Abstract: This paper covers reliability assessment of the fatigue life of a bridge-deck section based on the statistical analysis of the strain-time histories measured by the structural health monitoring system permanently installed on the long-span steel bridge under study. Through statistical analysis of online strain responses in the frequency domain using multiple linear regression, a representative block of daily cycles of strain history is obtained. It is further assumed that all cycles of online strain response during bridge service are repetitions of the representative block. The rain-flow counting method is then used to determine the stress spectrum of the representative block of daily cycles. The primary assessment of fatigue life at a given value of failure probability is undertaken for the sample component in a bridge-deck section by using the classification of details for welded bridge components and the associated statistical fatigue model provided by the British Standard BS5400. In order to evaluate bridge fatigue at any value of failure probability, a modified probability model is proposed based on BS5400. The fatigue life of the considered component in the bridge-deck section is then evaluated for some other values of probability of failure which are not included in BS5400 by use of the modified probability model. The analytical results show that the modified model is practical for reliable evaluation of the service life of existing bridges under random traffic loading.