Structural health monitoring (SHM) can continuously and nondestructively evaluate the state and performance of structures using the structural responses to external loads or environmental conditions. Moreover, online or real-time SHM of civil structures provides significant advantages over periodic or manual inspection methods, especially under disaster loadings, where the consequences of failure can be severe. To achieve it, performing system identification and damage detection recursively, said recursive subspace identification (RSI), is a promising solution, and SHM based on the algorithms can evaluate damage or deterioration of civil structures, give insight into the health and performance of a structural system, and provide valuable information for decision-making on maintenance and repair. However, the time-consuming decompositions frustrate these algorithms. As a compromise, additional processing is required to implement online and real-time applications. This study demonstrates a modified algorithm that takes advantage of the projection approximation subspace tracking (PAST) algorithm and the repeated system matrices in the extended observability matrix. The modification can reduce numerical decompositions and improve important timeliness for online or real-time SHM of civil structures. Both the numerical simulation and experimental investigation have been used to verify the proposed method, and the results show its capability to determine the changes in the dynamic characteristics of a structure in either the laboratory experiment or in the field application. In the last place, the discussion and some conclusions are also drawn in this paper.
{"title":"Development of Recursive Subspace Identification for Real-Time Structural Health Monitoring under Seismic Loading","authors":"Shieh-Kung Huang, Fu-Chung Chi","doi":"10.1155/2023/1117042","DOIUrl":"https://doi.org/10.1155/2023/1117042","url":null,"abstract":"Structural health monitoring (SHM) can continuously and nondestructively evaluate the state and performance of structures using the structural responses to external loads or environmental conditions. Moreover, online or real-time SHM of civil structures provides significant advantages over periodic or manual inspection methods, especially under disaster loadings, where the consequences of failure can be severe. To achieve it, performing system identification and damage detection recursively, said recursive subspace identification (RSI), is a promising solution, and SHM based on the algorithms can evaluate damage or deterioration of civil structures, give insight into the health and performance of a structural system, and provide valuable information for decision-making on maintenance and repair. However, the time-consuming decompositions frustrate these algorithms. As a compromise, additional processing is required to implement online and real-time applications. This study demonstrates a modified algorithm that takes advantage of the projection approximation subspace tracking (PAST) algorithm and the repeated system matrices in the extended observability matrix. The modification can reduce numerical decompositions and improve important timeliness for online or real-time SHM of civil structures. Both the numerical simulation and experimental investigation have been used to verify the proposed method, and the results show its capability to determine the changes in the dynamic characteristics of a structure in either the laboratory experiment or in the field application. In the last place, the discussion and some conclusions are also drawn in this paper.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"20 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139244121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heying Qin, Chunde Li, Jianqiang Zhu, Boguang Luo, Feng Fu
In this paper, a new fiber Bragg grating (FBG) strain sensor with adjustable sensitivity is invented. The sensitivity adjustment, strain sensing, and temperature compensation principles of the sensor and the corresponding formulae are developed. The prototype sensor specimen is developed, and a series of tests are performed to investigate its strain sensitivity and temperature compensation characteristics. The results show that the strain sensitivity of the sensor can be adjusted effectively by the correspondent L/LFBG parameter, with an acceptable discrepancy within ±5% of the theoretical value. The linearity, repeatability, and hysteresis were analyzed, and the errors were 0.98%, 1.15%, and 0.09%, respectively, with excellent performance. When the temperature difference was 20°C, through temperature compensation calibration, the error between the monitored strain and the actual strain was within 5% after temperature compensation correction, showing that this new type of FBG strain sensor can meet the strain monitoring needs of various engineering structures and provide reliable data acquisition.
{"title":"Development of a High-Sensitivity and Adjustable FBG Strain Sensor for Structural Monitoring","authors":"Heying Qin, Chunde Li, Jianqiang Zhu, Boguang Luo, Feng Fu","doi":"10.1155/2023/6665803","DOIUrl":"https://doi.org/10.1155/2023/6665803","url":null,"abstract":"In this paper, a new fiber Bragg grating (FBG) strain sensor with adjustable sensitivity is invented. The sensitivity adjustment, strain sensing, and temperature compensation principles of the sensor and the corresponding formulae are developed. The prototype sensor specimen is developed, and a series of tests are performed to investigate its strain sensitivity and temperature compensation characteristics. The results show that the strain sensitivity of the sensor can be adjusted effectively by the correspondent L/LFBG parameter, with an acceptable discrepancy within ±5% of the theoretical value. The linearity, repeatability, and hysteresis were analyzed, and the errors were 0.98%, 1.15%, and 0.09%, respectively, with excellent performance. When the temperature difference was 20°C, through temperature compensation calibration, the error between the monitored strain and the actual strain was within 5% after temperature compensation correction, showing that this new type of FBG strain sensor can meet the strain monitoring needs of various engineering structures and provide reliable data acquisition.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"69 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139273120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Da-You Duan, K. S. C. Kuang, Zuo-Cai Wang, Xiao-Tong Sun
Noncontact measurement techniques in structural dynamics field have progressed significantly in the past few decades. Vision-based measurement techniques are unique in that they have the ability to achieve full-field measurement and possess the typical advantages associated with noncontact measurement techniques. Recently, vision-based techniques have also been applied to streaming of videos for structural dynamic displacement measurement. The most recent trends in vision-based measurements include target tracing, digital image correlation, and target-less approaches. There are, however, some shortcomings of the vision-based techniques such as susceptibilities to image noise, prevailing light conditions, and limit in measurement resolution. To reduce these shortcomings, a method known as video motion magnification (MM) can be used to amplify small structural motions. Using the phase-based motion magnification (PBMM) and subpixel edge detection methods, the full-field dynamic displacements of the structure can be obtained. The deep convolutional long short-term memory (ConvLSTM) network is applied to aid in the selection of the frequency band for magnification in the PBMM algorithm. To achieve higher measurement accuracy, the displacement results with and without MM are combined with the finite impulse response (FIR) filter which can reduce the error caused by the PBMM procedure. In the tests, plastic optical fiber (POF) displacement sensors are introduced and used as reference measurements to compare the dynamic displacement results from the proposed vision-based method. Compared with the measured displacements with POF sensors, the proposed method offers high level of accuracy for full-field displacement measurement.
{"title":"Video Motion Magnification and Subpixel Edge Detection-Based Full-Field Dynamic Displacement Measurement","authors":"Da-You Duan, K. S. C. Kuang, Zuo-Cai Wang, Xiao-Tong Sun","doi":"10.1155/2023/7904198","DOIUrl":"https://doi.org/10.1155/2023/7904198","url":null,"abstract":"Noncontact measurement techniques in structural dynamics field have progressed significantly in the past few decades. Vision-based measurement techniques are unique in that they have the ability to achieve full-field measurement and possess the typical advantages associated with noncontact measurement techniques. Recently, vision-based techniques have also been applied to streaming of videos for structural dynamic displacement measurement. The most recent trends in vision-based measurements include target tracing, digital image correlation, and target-less approaches. There are, however, some shortcomings of the vision-based techniques such as susceptibilities to image noise, prevailing light conditions, and limit in measurement resolution. To reduce these shortcomings, a method known as video motion magnification (MM) can be used to amplify small structural motions. Using the phase-based motion magnification (PBMM) and subpixel edge detection methods, the full-field dynamic displacements of the structure can be obtained. The deep convolutional long short-term memory (ConvLSTM) network is applied to aid in the selection of the frequency band for magnification in the PBMM algorithm. To achieve higher measurement accuracy, the displacement results with and without MM are combined with the finite impulse response (FIR) filter which can reduce the error caused by the PBMM procedure. In the tests, plastic optical fiber (POF) displacement sensors are introduced and used as reference measurements to compare the dynamic displacement results from the proposed vision-based method. Compared with the measured displacements with POF sensors, the proposed method offers high level of accuracy for full-field displacement measurement.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82203401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
X. Feng, Yangbiao Fan, Haijun Peng, Yao Chen, Yiwen Zheng
Active vibration control of tensegrity structures is often challenging due to the geometrical nonlinearity, assemblage uncertainties of connections, and actuator saturation of controllers. To tackle these technical difficulties, a fast model predictive control (FMPC) strategy is herein implemented to effectively mitigate the structural vibration. Specifically, based on the explicit expression form of the Newmark- β method, the computation of the matrix exponential is avoided and replaced by one online and two offline transient analyses at each sampling instant on the structure, and the optimal control input is attainted from the second-order dynamic equation without forming an expanded state-space equation. Meanwhile, the artificial fish swarm algorithm (AFSA) is embedded to automatically derive optimal arrangement of actuators with the selection of a reasonable objective function. Two illustrative examples, including two standard and clustered tensegrity beams and a clustered tensegrity tower, have been fully investigated. The outcomes from illustrative examples prove the effectiveness and feasibility of the proposed method in optimal active vibration control of tensegrity structures, implying a promising prospect of the investigated approach in analyzing and solving relevant engineering problems.
{"title":"Optimal Active Vibration Control of Tensegrity Structures Using Fast Model Predictive Control Strategy","authors":"X. Feng, Yangbiao Fan, Haijun Peng, Yao Chen, Yiwen Zheng","doi":"10.1155/2023/2076738","DOIUrl":"https://doi.org/10.1155/2023/2076738","url":null,"abstract":"Active vibration control of tensegrity structures is often challenging due to the geometrical nonlinearity, assemblage uncertainties of connections, and actuator saturation of controllers. To tackle these technical difficulties, a fast model predictive control (FMPC) strategy is herein implemented to effectively mitigate the structural vibration. Specifically, based on the explicit expression form of the Newmark-\u0000 \u0000 β\u0000 \u0000 method, the computation of the matrix exponential is avoided and replaced by one online and two offline transient analyses at each sampling instant on the structure, and the optimal control input is attainted from the second-order dynamic equation without forming an expanded state-space equation. Meanwhile, the artificial fish swarm algorithm (AFSA) is embedded to automatically derive optimal arrangement of actuators with the selection of a reasonable objective function. Two illustrative examples, including two standard and clustered tensegrity beams and a clustered tensegrity tower, have been fully investigated. The outcomes from illustrative examples prove the effectiveness and feasibility of the proposed method in optimal active vibration control of tensegrity structures, implying a promising prospect of the investigated approach in analyzing and solving relevant engineering problems.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"152 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86896464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The unscented Kalman filter (UKF) serves as an efficient estimator widely utilized for the recursive identification of parameters. However, the UKF is not well suited for tracking time-variant parameters. Moreover, the unscented transformation (UT) used in the UKF typically relies on Cholesky decomposition to perform the square root operation of the covariance matrix. This method necessitates the matrix to maintain symmetry and positive definiteness. Due to the adverse influence of rounding error and noise, it becomes challenging to guarantee the positive definiteness of the matrix in each recursive step for practical engineering. The square root UKF (SRUKF) eliminates the need for the square root operation in the UT by directly updating the square root of the covariance matrix during each recursion. However, the SRUKF still relies on the rank 1 update to the Cholesky factorization to perform the recursive process, which also necessitates the matrix to be positive definite. Furthermore, the SRUKF is ineffective in the identification of time-variant parameters. Therefore, this paper proposes a modification to the SRUKF that ensures unconditional numerical stability by utilizing QR decomposition. Subsequently, the modified square root UKF (MSRUKF) method is enhanced by incorporating an adaptive forgetting factor that can be adjusted based on the residual information from each recursive step. This adaptation leads to the development of the adaptive SRUKF with forgetting factor (ASRUKF-FF) method, which significantly improves the tracking capability for time-variant parameters. To validate the effectiveness of the proposed method, this paper demonstrates its application in identifying the time-variant stiffness and damping parameters of a three-story frame structure. In addition, the method is employed to estimate the time-variant stiffness of the bridge excited by vehicles. The simulation results show that the proposed method has the superiority of high accuracy, strong robustness, and widespread applicability, even with incomplete measurements and inappropriate parameter settings.
{"title":"A Novel Adaptive Square Root UKF with Forgetting Factor for the Time-Variant Parameter Identification","authors":"Yanzhe Zhang, Yong Ding, Jianqing Bu, Lina Guo","doi":"10.1155/2023/4160146","DOIUrl":"https://doi.org/10.1155/2023/4160146","url":null,"abstract":"The unscented Kalman filter (UKF) serves as an efficient estimator widely utilized for the recursive identification of parameters. However, the UKF is not well suited for tracking time-variant parameters. Moreover, the unscented transformation (UT) used in the UKF typically relies on Cholesky decomposition to perform the square root operation of the covariance matrix. This method necessitates the matrix to maintain symmetry and positive definiteness. Due to the adverse influence of rounding error and noise, it becomes challenging to guarantee the positive definiteness of the matrix in each recursive step for practical engineering. The square root UKF (SRUKF) eliminates the need for the square root operation in the UT by directly updating the square root of the covariance matrix during each recursion. However, the SRUKF still relies on the rank 1 update to the Cholesky factorization to perform the recursive process, which also necessitates the matrix to be positive definite. Furthermore, the SRUKF is ineffective in the identification of time-variant parameters. Therefore, this paper proposes a modification to the SRUKF that ensures unconditional numerical stability by utilizing QR decomposition. Subsequently, the modified square root UKF (MSRUKF) method is enhanced by incorporating an adaptive forgetting factor that can be adjusted based on the residual information from each recursive step. This adaptation leads to the development of the adaptive SRUKF with forgetting factor (ASRUKF-FF) method, which significantly improves the tracking capability for time-variant parameters. To validate the effectiveness of the proposed method, this paper demonstrates its application in identifying the time-variant stiffness and damping parameters of a three-story frame structure. In addition, the method is employed to estimate the time-variant stiffness of the bridge excited by vehicles. The simulation results show that the proposed method has the superiority of high accuracy, strong robustness, and widespread applicability, even with incomplete measurements and inappropriate parameter settings.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77044703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image-based bridge displacement measurement still suffers from certain limitations in outdoor implementation. Each of these limitations was addressed in this study. (1) The laser spot is difficult to identify visually during the object distance (OD: mm) measurement using a laser rangefinder, which makes the scale factor (SF: mm/pixel) calibration tricky. To overcome this issue, a stereovision-based full-field OD measurement method using only one camera was suggested. (2) Sunlight reflected by the water surface during the measurement causes light spot interference on the captured images, which is not conducive to target tracking. A network for light spot removal based on a generative adversarial network (GAN) is designed. To obtain a better image restoration effect, the edge prior was novelly designed as the input of a shadow mask-based semantic-aware network (S2Net). (3) A coarse-to-fine matching strategy combined with image sparse representation (SR) was developed to balance the subpixel location precision and efficiency. The effectiveness of the above innovations was verified through algorithm evaluation. Finally, the integrated method was applied to the vibration response monitoring of a concrete bridge impacted by the traffic load. The image-based measurement results show good agreement with those of the long-gauge fiber Bragg grating sensors and lower noise than that of the method before improvement.
{"title":"Bridge Displacement Measurement Using the GAN-Network-Based Spot Removal Algorithm and the SR-Based Coarse-to-Fine Target Location Method","authors":"Shanshan Yu, Jian Zhang","doi":"10.1155/2023/6035288","DOIUrl":"https://doi.org/10.1155/2023/6035288","url":null,"abstract":"Image-based bridge displacement measurement still suffers from certain limitations in outdoor implementation. Each of these limitations was addressed in this study. (1) The laser spot is difficult to identify visually during the object distance (OD: mm) measurement using a laser rangefinder, which makes the scale factor (SF: mm/pixel) calibration tricky. To overcome this issue, a stereovision-based full-field OD measurement method using only one camera was suggested. (2) Sunlight reflected by the water surface during the measurement causes light spot interference on the captured images, which is not conducive to target tracking. A network for light spot removal based on a generative adversarial network (GAN) is designed. To obtain a better image restoration effect, the edge prior was novelly designed as the input of a shadow mask-based semantic-aware network (S2Net). (3) A coarse-to-fine matching strategy combined with image sparse representation (SR) was developed to balance the subpixel location precision and efficiency. The effectiveness of the above innovations was verified through algorithm evaluation. Finally, the integrated method was applied to the vibration response monitoring of a concrete bridge impacted by the traffic load. The image-based measurement results show good agreement with those of the long-gauge fiber Bragg grating sensors and lower noise than that of the method before improvement.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85897296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Min-Yuan Cheng, Akhmad F. K. Khitam, Nan-Chieh Wang
Constructing tunnels in urban spaces usually uses shield tunneling. Because of numerous uncertainties related to underground construction, appropriate monitoring systems are required to prevent disasters from happening. This study collected the settlement monitoring data for Tender CG291 of the Songshan Line of the Taipei Mass Rapid Transit (MRT) system and considered that influential factors were examined to identify the correlations between predictor variables and settlement outcomes. An inference model based on symbiotic organisms search-least squares support vector machine (SOS-LSSVM) was proposed and trained on the collected data. Moreover, because the dataset used for this study contained far less data at the alert level than at the safe level, the class of the dataset was imbalanced, which could compromise the classification accuracy. This study also employed the probability distribution data balance sampling methods to enhance the forecast accuracy. The results showed that the SOS-LSSVM exhibited the most favorable accuracy compared to four other artificial intelligence-based inference models. Therefore, the proposed model can serve as an early warning reference in tunnel design and construction work.
{"title":"Self-Tuning Inference Model for Settlement in Shield Tunneling: A Case Study of the Taipei Mass Rapid Transit System’s Songshan Line","authors":"Min-Yuan Cheng, Akhmad F. K. Khitam, Nan-Chieh Wang","doi":"10.1155/2023/6780235","DOIUrl":"https://doi.org/10.1155/2023/6780235","url":null,"abstract":"Constructing tunnels in urban spaces usually uses shield tunneling. Because of numerous uncertainties related to underground construction, appropriate monitoring systems are required to prevent disasters from happening. This study collected the settlement monitoring data for Tender CG291 of the Songshan Line of the Taipei Mass Rapid Transit (MRT) system and considered that influential factors were examined to identify the correlations between predictor variables and settlement outcomes. An inference model based on symbiotic organisms search-least squares support vector machine (SOS-LSSVM) was proposed and trained on the collected data. Moreover, because the dataset used for this study contained far less data at the alert level than at the safe level, the class of the dataset was imbalanced, which could compromise the classification accuracy. This study also employed the probability distribution data balance sampling methods to enhance the forecast accuracy. The results showed that the SOS-LSSVM exhibited the most favorable accuracy compared to four other artificial intelligence-based inference models. Therefore, the proposed model can serve as an early warning reference in tunnel design and construction work.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"12 10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82618475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of strain modes in structural health monitoring has been constantly increasing because of their superior sensitivity to local structural anomalies. This study aims to investigate the applicability and robustness of power spectral density transmissibility (PSDT) in operational strain modal analysis (OSMA). By noting that OSMA in the frequency domain is vulnerable to the error of spectral estimates, uncertainty quantification stemming from strain spectral estimates and the error propagation analysis in OSMA are conducted from an analytical perspective. The main contributions include the following: (i) the mean and variance of strain PSDT estimates are asymptotically derived based on statistical moment theory and the statistics of PSD estimate error, (ii) the coefficients of variation (c.o.v.) of the strain PSDT estimate and strain spectral estimates are compared with each other through asymptotic analysis to elaborate the robustness of strain PSDT, and (iii) the variability of the strain mode shape is quantified based on the asymptotic formula of strain PSDT estimates tending to local minima of asymptotic zero variance at the resonances. The accuracy and efficiency of the quantification and propagation analysis are validated through numerical and experimental test data accompanied by various parametric studies.
{"title":"Quantification of Statistical Error in the Estimate of Strain Power Spectral Density Transmissibility for Operational Strain Modal Analysis","authors":"Q. Sun, W. Yan, W. Ren, Lin-Bo Cao, Hai-Yi Wu","doi":"10.1155/2023/6661720","DOIUrl":"https://doi.org/10.1155/2023/6661720","url":null,"abstract":"The use of strain modes in structural health monitoring has been constantly increasing because of their superior sensitivity to local structural anomalies. This study aims to investigate the applicability and robustness of power spectral density transmissibility (PSDT) in operational strain modal analysis (OSMA). By noting that OSMA in the frequency domain is vulnerable to the error of spectral estimates, uncertainty quantification stemming from strain spectral estimates and the error propagation analysis in OSMA are conducted from an analytical perspective. The main contributions include the following: (i) the mean and variance of strain PSDT estimates are asymptotically derived based on statistical moment theory and the statistics of PSD estimate error, (ii) the coefficients of variation (c.o.v.) of the strain PSDT estimate and strain spectral estimates are compared with each other through asymptotic analysis to elaborate the robustness of strain PSDT, and (iii) the variability of the strain mode shape is quantified based on the asymptotic formula of strain PSDT estimates tending to local minima of asymptotic zero variance at the resonances. The accuracy and efficiency of the quantification and propagation analysis are validated through numerical and experimental test data accompanied by various parametric studies.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87035210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Donev, Rodrigo Díaz Flores, L. Eberhardsteiner, Luis Zelaya-Lainez, C. Hellmich, Martin Buchta, B. Pichler
Falling weight deflectometer (FWD) tests are performed worldwide for assessing the health of pavement structures. Interpretation of FWD-measured surface deflections turns out to be challenging because the behavior of pavement structures is temperature-dependent. In order to investigate the influence of temperature on the overall pavement performance and on the stiffness of individual layers, temperature sensors, asphalt strain gauges, and accelerometers were installed into one rigid (concrete) and two flexible (asphalt) pavement structures, mostly at layer interfaces. Three different methods for installation of the strain gauges are compared. From correspondingly gained experience, it is recommended to install a steel dummy as a place-holder into the surface of hot asphalt layers, immediately after their construction and right before their compaction, and to replace the dummy with the actual sensor right before the installation of the next layer. Concerning the first data obtained from dynamic testing at the field-testing sites, FWD tests performed at different temperatures deliver, as expected, different surface deflections. As for the rigid pavement, sledgehammer strokes onto a metal plate, transmitted to the pavement via a rubber pad, yield accelerometer readings that allow for detection of curling (=temperature-gradient-induced partial loss of contact of the concrete slab from lower layers). In the absence of curling, the here-proposed sledgehammer tests yield accelerometer readings that allow for quantification of the runtime of longitudinal waves through asphalt, cement-stabilized, and unbound layers, such that their stiffness can be quantified using the theory of elastic wave propagation through isotropic media.
{"title":"Instrumentation of Field-Testing Sites for Dynamic Characterization of the Temperature-Dependent Stiffness of Pavements and Their Layers","authors":"V. Donev, Rodrigo Díaz Flores, L. Eberhardsteiner, Luis Zelaya-Lainez, C. Hellmich, Martin Buchta, B. Pichler","doi":"10.1155/2023/2857660","DOIUrl":"https://doi.org/10.1155/2023/2857660","url":null,"abstract":"Falling weight deflectometer (FWD) tests are performed worldwide for assessing the health of pavement structures. Interpretation of FWD-measured surface deflections turns out to be challenging because the behavior of pavement structures is temperature-dependent. In order to investigate the influence of temperature on the overall pavement performance and on the stiffness of individual layers, temperature sensors, asphalt strain gauges, and accelerometers were installed into one rigid (concrete) and two flexible (asphalt) pavement structures, mostly at layer interfaces. Three different methods for installation of the strain gauges are compared. From correspondingly gained experience, it is recommended to install a steel dummy as a place-holder into the surface of hot asphalt layers, immediately after their construction and right before their compaction, and to replace the dummy with the actual sensor right before the installation of the next layer. Concerning the first data obtained from dynamic testing at the field-testing sites, FWD tests performed at different temperatures deliver, as expected, different surface deflections. As for the rigid pavement, sledgehammer strokes onto a metal plate, transmitted to the pavement via a rubber pad, yield accelerometer readings that allow for detection of curling (=temperature-gradient-induced partial loss of contact of the concrete slab from lower layers). In the absence of curling, the here-proposed sledgehammer tests yield accelerometer readings that allow for quantification of the runtime of longitudinal waves through asphalt, cement-stabilized, and unbound layers, such that their stiffness can be quantified using the theory of elastic wave propagation through isotropic media.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81131204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The displacement of concrete dams effectively reflects their structural integrity and operational status. Therefore, establishing a model for predicting the displacement of concrete dams and studying the evolution mechanism of dam displacement is essential for monitoring the structural safety of dams. Current data-driven models utilize artificial data that cannot reflect the actual status of dams for network training. They also have difficulty extracting the temporal patterns from long-term dependencies and obtaining the interactions between the targets and variables. To address such problems, we propose a novel model for predicting the displacement of dams based on the temporal convolutional network (TCN) with the attention mechanism and multioutput regression branches, named MLA-TCN (where MLA is multioutput model with attention mechanism). The attention mechanism implements information screening and weight distribution based on the importance of the input variables. The TCN extracts long-term temporal information using the dilated causal convolutional network and residual connection, and the multioutput regression branch achieves simultaneous multitarget prediction by establishing multiple regression tasks. Finally, the applicability of the proposed model is demonstrated using data on a concrete gravity dam within 14 years, and its accuracy is validated by comparing it with seven state-of-the-art benchmarks. The results show that the MLA-TCN model, with a mean absolute error (MAE) of 0.05 mm, a root-mean-square error (RMSE) of 0.07 mm, and a coefficient of determination (R2) of 0.99, has a comparably high predictive capability and outperforms the benchmarks, providing an accurate and effective method to estimate the displacement of dams.
{"title":"MLA-TCN: Multioutput Prediction of Dam Displacement Based on Temporal Convolutional Network with Attention Mechanism","authors":"Yu Wang, Guohua Liu","doi":"10.1155/2023/2189912","DOIUrl":"https://doi.org/10.1155/2023/2189912","url":null,"abstract":"The displacement of concrete dams effectively reflects their structural integrity and operational status. Therefore, establishing a model for predicting the displacement of concrete dams and studying the evolution mechanism of dam displacement is essential for monitoring the structural safety of dams. Current data-driven models utilize artificial data that cannot reflect the actual status of dams for network training. They also have difficulty extracting the temporal patterns from long-term dependencies and obtaining the interactions between the targets and variables. To address such problems, we propose a novel model for predicting the displacement of dams based on the temporal convolutional network (TCN) with the attention mechanism and multioutput regression branches, named MLA-TCN (where MLA is multioutput model with attention mechanism). The attention mechanism implements information screening and weight distribution based on the importance of the input variables. The TCN extracts long-term temporal information using the dilated causal convolutional network and residual connection, and the multioutput regression branch achieves simultaneous multitarget prediction by establishing multiple regression tasks. Finally, the applicability of the proposed model is demonstrated using data on a concrete gravity dam within 14 years, and its accuracy is validated by comparing it with seven state-of-the-art benchmarks. The results show that the MLA-TCN model, with a mean absolute error (MAE) of 0.05 mm, a root-mean-square error (RMSE) of 0.07 mm, and a coefficient of determination (R2) of 0.99, has a comparably high predictive capability and outperforms the benchmarks, providing an accurate and effective method to estimate the displacement of dams.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"234 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72938233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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