A New Method for Separating Temperature Effect of Bridge Strain Monitoring

Lei Huang,Jingzhou Xin,Jiafeng Yang,Shuangjiang Li,Jianting Zhou 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.8

Temperature has a significant influence on bridge strain monitoring data. To improve the accuracy of temperature effect separation in strain monitoring data, this paper proposes a temperature effect separation method comprising variational nonlinear chirp mode decomposition (VNCMD), principal component analysis (PCA) and blind source separation. Firstly, VNCMD was used to decompose the monitoring data of strain and temperature, and the intrinsic mode functions (IMF) of strain and temperature signals were obtained. Secondly, PCA was used to reduce the dimension of IMF, and the false components were eliminated to select the optimal components. After reducing the dimension, the components were used as the input of fast independent component analysis model for blind source separation. Finally, the feasibility and accuracy of the method was verified via the signal-to-noise ratio (SNR) in the simulated signal, and the separation results were evaluated using the Pearson correlation coefficient between the strain component and the corresponding temperature component in real bridge monitoring data. The proposed method performed better than the empirical mode decomposition (EMD) method. The signal-to-noise ratio (SNR) of VNCMD improved 51.80% for daily temperature difference effect and 32.41% for annual temperature difference effect in the numerical study, respectively; the correlation coefficients of VNCMD improved 52.90% for daily temperature difference effect and 4.26% for annual temperature difference effect in practical verification, respectively.

Autoregressive Model-Based Structural Damage Identification and Localization Using Convolutional Neural Networks

Qizhi Tang,Jianting Zhou,Jingzhou Xin,Siyu Zhao,Yingxin Zhou 대한토목학회 2020 KSCE Journal of Civil Engineering Vol.24 No.7

Traditional autoregressive (AR) model-based damage identification methods construct structural damage sensitive features by trial and error, which are time-consuming, laborious and may lead to poor recognition effect. This study applies convolutional neural networks (CNNs) to quickly and automatically extract high-dimensional features of autoregressive model coefficients (ARMCs). In this research, AR model was utilized to fit the acceleration time series. The input matrices marked with damage location were produced by ARMCs, and then those matrices were sent to the proposed CNN for training. The trained CNN was employed for damage identification and localization. The effectiveness of the proposed method was verified by the damage identification and localization of a three-storied frame structure. The performance of the proposed CNN was compared with multilayer perception (MLP), random forest, and support vector machine (SVM). Meanwhile, the prediction results from different sample types were also discussed. Furthermore, parametric study in relation to the number of accelerometers and ARMCs used is conducted. These analyses demonstrate that the accuracy of CNN tests results reach 100%, 6.67%, 20%, and 25% higher than that of MLP, random forest, and SVM, respectively. Besides, other metrics calculated in this paper (e.g., precision, recall) further indicate that the proposed CNN performs well. The combination of AR and CNN does show excellent performance in damage identification and localization, which seems to be able to resist external excitation changes and accurately identify the multi-location damage and minor damage using limited accelerometers and ARMCs.

FDTD Simulation for Moisture Asphalt Pavement Thickness and Density Estimation Utilizing Ground Penetrating Radar

Lilong Cui,Tianqing Ling,Jingzhou Xin,Rukai Li 대한토목학회 2021 KSCE JOURNAL OF CIVIL ENGINEERING Vol.25 No.9

Ground penetrating radar (GPR) has the potential to estimate the thickness and density of asphalt pavement during compaction. However, the surface moisture sprayed by the compactor interferes with the accuracy of data collection significantly. This study proposed an approach based on the extended common midpoint (XCMP) method to minimize the effect of surface moisture. Both the numerical simulation of finite-difference time-domain (FDTD) and laboratory experiments were carried out to study the effect of the surface moisture on the GPR signal. Then, three FDTD models with different incident angles of GPR signal were established, and the difference of time intervals obtained from dry and moisture pavementswith each model was studied to propose a proper antennas installation mode. Finally, the thickness and density estimated using the proposed method and surface reflection method were compared to validate the accuracy of the proposed approach. The results show that: 1) FDTD models were verified to simulate the interaction of GPR signal with moisture pavement effectively; 2) the time interval of the GPR signal between the surface and bottom of AC layer increased as the thin wet layer dielectric constant grew, and remained unaffected by the electric conductivity of the thin wet layer; 3) the average error of thickness and density predicted utilizing the proposed method were less than 1.3% and 2.4%, respectively, undercomplicated compaction conditions. This study notes that compaction monitoring in real time could benefit from the proposed method.

A Numerical-Experimental Framework to Separate the Effects of Different Turbulence Components on the Buffeting Forces of Bluff-body Structures

Bo Wu,Jianting Zhou,Jingzhou Xin,Hong Zhang,Fengbo Wu 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.8

In conventional buffeting analyses of flexible civil engineering structures, the differencesbetween the effects of longitudinal (u-) and vertical (w-) turbulence components were usuallyignored. This assumption could cause unpredictable errors and needs to be refined. Toimprove the accuracy of buffeting analyses, this paper proposes a framework combiningComputational Fluid Dynamics simulation and wind tunnel test to analyze the buffeting forcesconsidering the differences between turbulence components. First, the Aerodynamic AdmittanceFunctions (AAFs) with respect to u- and w- turbulence are numerically evaluated. Next, thetotal buffeting forces are experimentally measured. Finally, the numerical and experimentalresults are combined following a theoretical scheme to separate the effects of u- and wturbulence. Results show that u- and w- turbulence have different contributions to the buffetingforces, directly indicating the inaccuracy of the conventional assumption. In the turbulencefield investigated, the buffeting lift force at zero angle of attack (AoA) are all contributed fromthe w-turbulence, while the contribution from u-turbulence increase as AoA increases. Thisnumerical-experimental framework overcomes the limitations of the conventional method byquantitatively describing the different contributions of u- and w- turbulence.

Experimental investigations on detecting lateral buckling for subsea pipelines with distributed fiber optic sensors

Jing Zhou,Xin Feng,Wenjing Wu,Xingyu Li,Xiaowei Zhang 국제구조공학회 2015 Smart Structures and Systems, An International Jou Vol.15 No.2

A methodology based on distributed fiber optic sensors is proposed to detect the lateral buckling for subsea pipelines in this study. Uncontrolled buckling may lead to serious consequences for the structural integrity of a pipeline. A simple solution to this problem is to control the formation of lateral buckles among the pipeline. This firms the importance of monitoring the occurrence and evolution of pipeline buckling during the installation stage and long-term service cycle. This study reports the experimental investigations on a method for distributed detection of lateral buckling in subsea pipelines with Brillouin fiber optic sensor. The sensing scheme possesses the capability for monitoring the pipeline over the entire structure. The longitudinal strains are monitored by mounting the Brillouin optical time domain analysis (BOTDA) distributed sensors on the outer surface of the pipeline. Then the bending-induced strain is extracted to detect the occurrence and evolution of lateral buckling. Feasibility of the method was validated by using an experimental program on a small scale model pipe. The results demonstrate that the proposed approach is able to detect, in a distributed manner, the onset and progress of lateral buckling in pipelines. The methodology developed in this study provides a promising tool for assessing the structural integrity of subsea pipelines