Pub Date : 2024-08-02DOI: 10.1177/13694332241267909
Tsukasa Mizutani, Shunsuke Iwai
Handheld Ground Penetrating Radar (GPR) is utilized for detecting rebar, but detecting damage is difficult due to its low reflectance. This study introduces an algorithm to quantitatively estimate damage thickness from GPR-received waveforms. Simple methods to separate peaks from time waveforms at the top and bottom of the crack prove challenging due to destructive interference and side lobes. In previous studies, it has been confirmed that minor variations in damage thickness affect the frequency property. We propose an algorithm to estimate damage thickness using pattern matching with a theoretical amplitude spectrum that accounts for multiple reflections. Initially, the damage thickness is roughly determined by combining low-frequency spectrum centroids with spectrum amplitude. After roughly estimating the damage thickness, subsequent spectral pattern matching is performed within predefined gating and bandwidth ranges. This approach enables quantitative estimation of damage thickness from 2 mm to 180 mm with a millimeter order accuracy, demonstrating its practical application potential.
{"title":"Accurate and fast damage thickness estimation in concrete using handheld GPR and spectral pattern matching","authors":"Tsukasa Mizutani, Shunsuke Iwai","doi":"10.1177/13694332241267909","DOIUrl":"https://doi.org/10.1177/13694332241267909","url":null,"abstract":"Handheld Ground Penetrating Radar (GPR) is utilized for detecting rebar, but detecting damage is difficult due to its low reflectance. This study introduces an algorithm to quantitatively estimate damage thickness from GPR-received waveforms. Simple methods to separate peaks from time waveforms at the top and bottom of the crack prove challenging due to destructive interference and side lobes. In previous studies, it has been confirmed that minor variations in damage thickness affect the frequency property. We propose an algorithm to estimate damage thickness using pattern matching with a theoretical amplitude spectrum that accounts for multiple reflections. Initially, the damage thickness is roughly determined by combining low-frequency spectrum centroids with spectrum amplitude. After roughly estimating the damage thickness, subsequent spectral pattern matching is performed within predefined gating and bandwidth ranges. This approach enables quantitative estimation of damage thickness from 2 mm to 180 mm with a millimeter order accuracy, demonstrating its practical application potential.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1177/13694332241269233
Xiao-Xue Li, Dan Li, Wei-Xin Ren, Xiang-Tao Sun
To ensure structural safety and integrity, a novel framework is developed for detecting the loosening of multi-bolt connections using wavelet entropy of vibro-acoustic modulation (VAM) signals. Wavelet entropy is employed as the dynamic index to capture the intricate time-frequency characteristics that are indicative of the connection status. Taking the wavelet entropy vectors as input, the proposed framework distinguishes itself by integrating a Transformer model for high-dimensional feature extraction with the recursive feature elimination (RFE) for essential feature selection, followed by a support vector machine (SVM) model for classification. Specifically, the Transformer model with innovative positional encoding capability helps to extract the time-dependent transient features that are sensitive to the bolt loosening. The RFE process reduces the data dimensionality while discerning the diagnostic information for more accurate classification. Through the experiment on a four-bolt joint, the identification results with cross-validation showed high accuracy and robustness of the proposed framework across various loosening cases. It outperformed the traditional SVM, long short-term memory network (LSTM), convolutional neural network (CNN)-SVM models without and with RFE, as well as the Transformer-SVM model without RFE, achieving an accuracy increase of 15.72%, 11.74%, 9.47%, 5.49%, and 5.06%, respectively. The proposed framework was demonstrated to be able to learn the damage-sensitive features more effectively from wavelet entropy data, marking a significant advancement in the health monitoring of engineering structures with high-strength bolt connections.
{"title":"An RFE-aided Transformer-SVM framework for multi-bolt connection loosening identification using wavelet entropy of vibro-acoustic modulation signals","authors":"Xiao-Xue Li, Dan Li, Wei-Xin Ren, Xiang-Tao Sun","doi":"10.1177/13694332241269233","DOIUrl":"https://doi.org/10.1177/13694332241269233","url":null,"abstract":"To ensure structural safety and integrity, a novel framework is developed for detecting the loosening of multi-bolt connections using wavelet entropy of vibro-acoustic modulation (VAM) signals. Wavelet entropy is employed as the dynamic index to capture the intricate time-frequency characteristics that are indicative of the connection status. Taking the wavelet entropy vectors as input, the proposed framework distinguishes itself by integrating a Transformer model for high-dimensional feature extraction with the recursive feature elimination (RFE) for essential feature selection, followed by a support vector machine (SVM) model for classification. Specifically, the Transformer model with innovative positional encoding capability helps to extract the time-dependent transient features that are sensitive to the bolt loosening. The RFE process reduces the data dimensionality while discerning the diagnostic information for more accurate classification. Through the experiment on a four-bolt joint, the identification results with cross-validation showed high accuracy and robustness of the proposed framework across various loosening cases. It outperformed the traditional SVM, long short-term memory network (LSTM), convolutional neural network (CNN)-SVM models without and with RFE, as well as the Transformer-SVM model without RFE, achieving an accuracy increase of 15.72%, 11.74%, 9.47%, 5.49%, and 5.06%, respectively. The proposed framework was demonstrated to be able to learn the damage-sensitive features more effectively from wavelet entropy data, marking a significant advancement in the health monitoring of engineering structures with high-strength bolt connections.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141887067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1177/13694332241267935
Yanbin Shen, Saihao You, Wucheng Xu, Yaozhi Luo
Structural health monitoring obtains data reflecting the service status of grid structures through sensors. One of the issues to consider in optimal sensor placement is how to obtain as much information as possible with a limited number of sensors. In this paper, a sensor placement method is proposed based on damage sensitivity and correlation analysis, which is based on strain energy calculation and is suitable for grid structures. Specifically, with the sensor locations as optimization variables, a mathematical optimization model is established by considering the damage sensitivity and redundancy of the monitoring scheme, and a genetic algorithm is employed for computation. Two examples, including a lattice shell and a flat grid, are provided to illustrate the method, followed by a discussion of the sensitivity of parameters such as stiffness reduction degree and load form. The results indicate that the redundancy of the optimized schemes for the two examples decreased by approximately 80% and 30%, respectively. The proposed method ensures a certain degree of damage sensitivity while significantly reducing redundancy, demonstrating its applicability and robustness in sensor placement for grid structures.
{"title":"Research on optimal sensor placement method for grid structures based on member strain energy","authors":"Yanbin Shen, Saihao You, Wucheng Xu, Yaozhi Luo","doi":"10.1177/13694332241267935","DOIUrl":"https://doi.org/10.1177/13694332241267935","url":null,"abstract":"Structural health monitoring obtains data reflecting the service status of grid structures through sensors. One of the issues to consider in optimal sensor placement is how to obtain as much information as possible with a limited number of sensors. In this paper, a sensor placement method is proposed based on damage sensitivity and correlation analysis, which is based on strain energy calculation and is suitable for grid structures. Specifically, with the sensor locations as optimization variables, a mathematical optimization model is established by considering the damage sensitivity and redundancy of the monitoring scheme, and a genetic algorithm is employed for computation. Two examples, including a lattice shell and a flat grid, are provided to illustrate the method, followed by a discussion of the sensitivity of parameters such as stiffness reduction degree and load form. The results indicate that the redundancy of the optimized schemes for the two examples decreased by approximately 80% and 30%, respectively. The proposed method ensures a certain degree of damage sensitivity while significantly reducing redundancy, demonstrating its applicability and robustness in sensor placement for grid structures.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1177/13694332241269259
Chul-Woo Kim, Shinya Kimura, Hiroki Sugiyama, Akinori Sato, Kazuyuki Ono
This study was conducted to investigate the vibration serviceability and impact coefficient of a seven-span continuous cable-stayed bridge planned for an expressway extension using a three-dimensional vehicle–bridge coupled vibration analysis. For the bridge design, deflection under the designed live load of the continuous cable-stayed bridge did not meet the deflection limit specified in “Japanese Design Specifications for Highway Bridges.” The excessive deflection indicates the possibility of poor vibration serviceability. To clarify the bridge vibration serviceability, the dynamic responses of the bridge and passing vehicles were examined using the three-dimensional vehicle–bridge coupled vibration analysis. The three-dimensional analysis was validated by comparing the vibration response of a single-span steel cable-stayed bridge in service subjected to vehicle running tests with those numerical responses from the three-dimensional analysis. The ride comfort of vehicles on the bridge was assessed in terms of vibration serviceability according to the ISO 2613-1 international standard for evaluating whole-body vibration exposure. The observation from the simulation-based investigation demonstrated that the vehicle response does not exceed the ride comfort limit irrespective of vehicle, road, and running conditions. In other words, the findings confirmed a negligible effect of large deflections on driving safety. The impact factors were found to be less than 1.05 for the main girder, less than 1.03 for the main tower base, and less than 1.04 for the cable. The impact factor was greatest when several vehicles were running at resonant headway.
本研究采用三维车辆-桥梁耦合振动分析方法，对计划用于高速公路扩建的七跨连续斜拉桥的振动适用性和冲击系数进行了研究。在桥梁设计中，连续斜拉桥在设计活载作用下的挠度不符合 "日本公路桥梁设计规范 "中规定的挠度限值。过大的挠度表明振动适用性可能很差。为明确桥梁的振动适用性，采用三维车辆-桥梁耦合振动分析法对桥梁和过往车辆的动态响应进行了研究。通过比较一座单跨钢斜拉桥在车辆行驶试验中的振动响应与三维分析得出的数值响应，验证了三维分析的有效性。根据 ISO 2613-1 评估全身振动暴露的国际标准，从振动适用性的角度对桥梁上车辆的乘坐舒适性进行了评估。模拟调查的结果表明，无论车辆、道路和运行条件如何，车辆的响应都不会超过乘坐舒适性的限制。换句话说，研究结果证实大偏差对驾驶安全的影响可以忽略不计。研究发现，主梁的影响系数小于 1.05，主塔基的影响系数小于 1.03，拉索的影响系数小于 1.04。当多辆车以共振车速行驶时，影响系数最大。
{"title":"Ride comfort and impact factor of a seven-span continuous cable-stayed bridge","authors":"Chul-Woo Kim, Shinya Kimura, Hiroki Sugiyama, Akinori Sato, Kazuyuki Ono","doi":"10.1177/13694332241269259","DOIUrl":"https://doi.org/10.1177/13694332241269259","url":null,"abstract":"This study was conducted to investigate the vibration serviceability and impact coefficient of a seven-span continuous cable-stayed bridge planned for an expressway extension using a three-dimensional vehicle–bridge coupled vibration analysis. For the bridge design, deflection under the designed live load of the continuous cable-stayed bridge did not meet the deflection limit specified in “Japanese Design Specifications for Highway Bridges.” The excessive deflection indicates the possibility of poor vibration serviceability. To clarify the bridge vibration serviceability, the dynamic responses of the bridge and passing vehicles were examined using the three-dimensional vehicle–bridge coupled vibration analysis. The three-dimensional analysis was validated by comparing the vibration response of a single-span steel cable-stayed bridge in service subjected to vehicle running tests with those numerical responses from the three-dimensional analysis. The ride comfort of vehicles on the bridge was assessed in terms of vibration serviceability according to the ISO 2613-1 international standard for evaluating whole-body vibration exposure. The observation from the simulation-based investigation demonstrated that the vehicle response does not exceed the ride comfort limit irrespective of vehicle, road, and running conditions. In other words, the findings confirmed a negligible effect of large deflections on driving safety. The impact factors were found to be less than 1.05 for the main girder, less than 1.03 for the main tower base, and less than 1.04 for the cable. The impact factor was greatest when several vehicles were running at resonant headway.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study aims to employ machine learning algorithms to analyze the axial bearing capacity of rubberized alkali-activated concrete filled steel tubes. A dataset encompassing 327 synthesized instances and seven input features is adopted for training and testing six machine learning models, including Decision Tree, Random Forest, Extremely Randomized Trees, Adaptive Boosting, Gradient Boosting Decision Trees (GBDT), and eXtreme Gradient Boosting Trees (XGBoost). The SHapley Additive exPlanation algorithm is employed to elucidate the prediction process of machine learning models and to analyze the influence of each parameter on axial bearing capacity. Comparison of evaluating metrics shows that GBDT and XGBoost models achieve highest accuracy and generalization capabilities when their Coefficient of Determination values surpassing 0.98 and Mean Absolute Percent Error remaining below 3%. Moreover, the explanation analysis of machine learning models reveals that diameter/width of the cross section, rubber content, yielding strength and thickness of steel tubes are critical variables that affect the axial bearing capacity, while compressive strength of alkali-activated concrete, specimen height, and shape of cross section show negligible impact. Besides, GBDT model overemphasizes the effect of specimen height and might lead a conservative prediction for specimens with smaller heights. Finally, compressive strength of alkali-activated concrete and diameter/width, thickness, and yielding strength of steel tubes are positively correlated with axial bearing capacity, and the increase of rubber content in alkali-activated concrete leads to the decrease of capacity.
{"title":"Data-driven axial bearing capacity analysis of steel tubes infilled with rubberized alkali-activated concrete","authors":"Chang Zhou, Xiao Tan, Yuzhou Zheng, Yuan Wang, Soroush Mahjoubi","doi":"10.1177/13694332241268243","DOIUrl":"https://doi.org/10.1177/13694332241268243","url":null,"abstract":"This study aims to employ machine learning algorithms to analyze the axial bearing capacity of rubberized alkali-activated concrete filled steel tubes. A dataset encompassing 327 synthesized instances and seven input features is adopted for training and testing six machine learning models, including Decision Tree, Random Forest, Extremely Randomized Trees, Adaptive Boosting, Gradient Boosting Decision Trees (GBDT), and eXtreme Gradient Boosting Trees (XGBoost). The SHapley Additive exPlanation algorithm is employed to elucidate the prediction process of machine learning models and to analyze the influence of each parameter on axial bearing capacity. Comparison of evaluating metrics shows that GBDT and XGBoost models achieve highest accuracy and generalization capabilities when their Coefficient of Determination values surpassing 0.98 and Mean Absolute Percent Error remaining below 3%. Moreover, the explanation analysis of machine learning models reveals that diameter/width of the cross section, rubber content, yielding strength and thickness of steel tubes are critical variables that affect the axial bearing capacity, while compressive strength of alkali-activated concrete, specimen height, and shape of cross section show negligible impact. Besides, GBDT model overemphasizes the effect of specimen height and might lead a conservative prediction for specimens with smaller heights. Finally, compressive strength of alkali-activated concrete and diameter/width, thickness, and yielding strength of steel tubes are positively correlated with axial bearing capacity, and the increase of rubber content in alkali-activated concrete leads to the decrease of capacity.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.1177/13694332241269250
Tonghao Zhang, Chenxi Xu, Didem Ozevin
Localizing defects in long-range pipelines is essential to reduce the inspection time and develop timely repair strategies. The acoustic emission (AE) method is employed to pinpoint the position of defects in pipelines. The conventional 1-D localization algorithm requires time of arrival differences between two sensors, which may not be accurately captured due to the dispersive nature of the pipe structures. The geometric variations such as elbows and welds can influence the propagating elastic waves and, consequently, arrival time. In this study, an AE source localization approach using a deep learning model is developed to tackle the influences of sensor-source distance and geometric variables. The multi-task learning model first identifies the impact of the elbow and subsequently integrates this information when predicting the source location. The proposed model is evaluated on a complex piping system, which features welded elbows in its connections. Incorporating the elbow effect into the model shows a notable improvement in overall accuracy, rising from 53% (conventional method) to 94% (proposed multi-task learning method).
{"title":"Acoustic emission source localization in complex pipe structure using multi-task deep learning models","authors":"Tonghao Zhang, Chenxi Xu, Didem Ozevin","doi":"10.1177/13694332241269250","DOIUrl":"https://doi.org/10.1177/13694332241269250","url":null,"abstract":"Localizing defects in long-range pipelines is essential to reduce the inspection time and develop timely repair strategies. The acoustic emission (AE) method is employed to pinpoint the position of defects in pipelines. The conventional 1-D localization algorithm requires time of arrival differences between two sensors, which may not be accurately captured due to the dispersive nature of the pipe structures. The geometric variations such as elbows and welds can influence the propagating elastic waves and, consequently, arrival time. In this study, an AE source localization approach using a deep learning model is developed to tackle the influences of sensor-source distance and geometric variables. The multi-task learning model first identifies the impact of the elbow and subsequently integrates this information when predicting the source location. The proposed model is evaluated on a complex piping system, which features welded elbows in its connections. Incorporating the elbow effect into the model shows a notable improvement in overall accuracy, rising from 53% (conventional method) to 94% (proposed multi-task learning method).","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.1177/13694332241268170
Fenghua Huang, Jinxiao Wang, Nianguan Teng, Bin Cheng
This paper investigates the dynamic response of low-medium-speed (LMS) maglev vehicle moving on the isolated bridge with lead rubber bearings (LRBs). In the vehicle-guideway bridge model, the vehicle is simulated as a 50-degree-of-freedom model consisting of a car-body and ten bogie modules. The guideway bridge with LRB is established by the finite element method, and the guideway is interacted with the vehicle by the actively controlled electromagnetic forces. The LRB is simulated by the nonlinear spring element for reflecting the hysteretic performance, and a fast nonlinear analysis (FNA) method is proposed to achieve the potential nonlinear behavior of LRB under vehicle load. Then, the dynamic response of maglev vehicle running on the isolated bridge with LRB is investigated and compared to that on the non-isolated bridge. The effect of vehicle speed and LRB isolation degree on the coupled system responses is studied. Furthermore, the driving quality of vehicle on LRB bridge is discussed, and the applicability of LRB to maglev line bridge is thoroughly evaluated. The results show that the installation of LRB exhibits relatively insignificant influence on the vertical response of vehicle-guideway system, while could enlarge the lateral response. The lateral response of coupled system is much more vulnerable to the isolation degree, and it is recommended that the isolation degree of LRB should not exceed 2.50 to guarantee the driving comfort and running safety.
{"title":"Coupled vibration of low-medium-speed maglev vehicle-guideway system on isolated bridge with lead rubber bearings","authors":"Fenghua Huang, Jinxiao Wang, Nianguan Teng, Bin Cheng","doi":"10.1177/13694332241268170","DOIUrl":"https://doi.org/10.1177/13694332241268170","url":null,"abstract":"This paper investigates the dynamic response of low-medium-speed (LMS) maglev vehicle moving on the isolated bridge with lead rubber bearings (LRBs). In the vehicle-guideway bridge model, the vehicle is simulated as a 50-degree-of-freedom model consisting of a car-body and ten bogie modules. The guideway bridge with LRB is established by the finite element method, and the guideway is interacted with the vehicle by the actively controlled electromagnetic forces. The LRB is simulated by the nonlinear spring element for reflecting the hysteretic performance, and a fast nonlinear analysis (FNA) method is proposed to achieve the potential nonlinear behavior of LRB under vehicle load. Then, the dynamic response of maglev vehicle running on the isolated bridge with LRB is investigated and compared to that on the non-isolated bridge. The effect of vehicle speed and LRB isolation degree on the coupled system responses is studied. Furthermore, the driving quality of vehicle on LRB bridge is discussed, and the applicability of LRB to maglev line bridge is thoroughly evaluated. The results show that the installation of LRB exhibits relatively insignificant influence on the vertical response of vehicle-guideway system, while could enlarge the lateral response. The lateral response of coupled system is much more vulnerable to the isolation degree, and it is recommended that the isolation degree of LRB should not exceed 2.50 to guarantee the driving comfort and running safety.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work examines the performance of reinforced concrete (RC) beams strengthened using bonded steel wire rope (SWR) at various prestressing levels. The strengthening approach has, however, been applied to the flexural strengthening of RC T-beams in the negative moment region, in order to determine its advantages. For this purpose, four RC T-beams were fabricated and tested under monotonic four-point bending: one control beam (S00), one beam strengthened with non-prestressed SWR (S20), and two beams strengthened with SWR (prestressed at 10% and 20% of their ultimate tensile strength: S21 and S22). The results indicate that the strengthened beams exhibit higher load-carrying capacities. Specifically, the cracking load, yield load, and ultimate load of S20, S21, and S22 increase by 10%–30%, 30%–50%, and 50%–90%, respectively, compared to S00. Additionally, there is an improvement in stiffness and energy absorption capacity. However, these strategies may have a dual effect on the specimens, resulting in a reduction in their ductility index. Finally, the tested beams were replicated using a three-dimensional finite element model, which has proved effective in predicting the behavior of such structures and, therefore, was found to be appropriate for use in future studies.
{"title":"Flexural performance of the negative moment region in bonded steel-wire-rope-strengthened reinforced concrete T-beams at different prestressing levels","authors":"Yanuar Haryanto, Gathot Heri Sudibyo, Laurencius Nugroho, Hsuan-Teh Hu, Ay Lie Han, Fu-Pei Hsiao, Arnie Widyaningrum, Yudi Susetyo","doi":"10.1177/13694332241268186","DOIUrl":"https://doi.org/10.1177/13694332241268186","url":null,"abstract":"This work examines the performance of reinforced concrete (RC) beams strengthened using bonded steel wire rope (SWR) at various prestressing levels. The strengthening approach has, however, been applied to the flexural strengthening of RC T-beams in the negative moment region, in order to determine its advantages. For this purpose, four RC T-beams were fabricated and tested under monotonic four-point bending: one control beam (S00), one beam strengthened with non-prestressed SWR (S20), and two beams strengthened with SWR (prestressed at 10% and 20% of their ultimate tensile strength: S21 and S22). The results indicate that the strengthened beams exhibit higher load-carrying capacities. Specifically, the cracking load, yield load, and ultimate load of S20, S21, and S22 increase by 10%–30%, 30%–50%, and 50%–90%, respectively, compared to S00. Additionally, there is an improvement in stiffness and energy absorption capacity. However, these strategies may have a dual effect on the specimens, resulting in a reduction in their ductility index. Finally, the tested beams were replicated using a three-dimensional finite element model, which has proved effective in predicting the behavior of such structures and, therefore, was found to be appropriate for use in future studies.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.1177/13694332241267903
Cailong Ma, Chuancang Wang, Chao-Lie Ning, Zhenyu Wang
The layout of the exterior retaining walls and external thermal insulation demands often introduce an eccentricity between the central axes of the beams and columns, leading to what is termed as an eccentric beam-column joint. Such the beam eccentricity is a key factor in shear failures within the joint region. This study addresses the negative impact of beam eccentricity on the shear capacity of reinforced concrete (RC) eccentric beam-column joints. Current research indicates that in the five prevailing shear capacity formulas of RC eccentric joints, adverse effects are primarily accommodated by either reducing the joint’s effective width ( bj) or implementing an eccentricity influence factor. The study challenges the validity of these approaches by dissecting the impact of singular and interactive factors, including. Eccentricity (2 e/ bc), beam-to-column width ratio ( bb/ bc), and the column’s aspect ratio ( hc/ bc). It was observed that while the influence of 2 e/ bc is generally well-accounted for, the effects of bb/ bc and hc/ bc are not adequately considered in the Chinese code and ACI-318. Leveraging the softened strut-and-tie model and insights from these examinations, a refined formula for determining the shear capacity of eccentric joints is introduced. This formula incorporates the detrimental effects of beam eccentricity through a newly conceptualized eccentric influence factor, which is a function of both 2 e/ bc and bb/ bc. Compared to existing models, this proposed formula also factors in the beam-to-column depth ratio and the longitudinal reinforcement in the middle of column section. Validation against experimental data from 26 eccentric joints demonstrates that the proposed formula yields predictions with the closest proximity to actual test results and the least variability compared to the five established formulas. This approach to considering the effects of e/ bc and bb/ bc proves to be slightly more accurate than Zhou’s model, making it a promising alternative for practical applications.
外部挡土墙的布局和外部隔热要求往往会在梁和柱的中心轴之间产生偏心，从而导致所谓的偏心梁柱连接。这种梁偏心是造成连接区域内剪切失效的关键因素。本研究探讨了梁偏心对钢筋混凝土（RC）偏心梁柱连接剪切能力的负面影响。目前的研究表明，在五种常用的 RC 偏心连接剪切承载力公式中，主要通过减小连接的有效宽度（bj）或采用偏心影响系数来消除不利影响。本研究通过剖析单一因素和交互因素的影响，对这些方法的有效性提出了质疑，这些因素包括研究发现，虽然 2 e/ bc 的影响一般都得到了充分考虑，但 bb/ bc 和 hc/ bc 的影响在中国规范和 ACI-318 中却没有得到充分考虑。利用软化支撑和拉杆模型以及从这些试验中获得的启示，引入了一个用于确定偏心接头抗剪承载力的改进公式。该公式通过一个新概念化的偏心影响系数（2 e/ bc 和 bb/ bc 的函数）纳入了梁偏心的不利影响。与现有模型相比，该公式还考虑了梁柱深度比和柱截面中部的纵向钢筋。根据 26 个偏心接头的实验数据进行的验证表明，与已建立的五个公式相比，所提出的公式得出的预测结果最接近实际测试结果，且变异性最小。事实证明，这种考虑 e/ bc 和 bb/ bc 影响的方法比周的模型略微精确，因此在实际应用中是一种很有前途的替代方法。
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Pub Date : 2024-07-24DOI: 10.1177/13694332241266541
Guojun Yang, Li Tian, Jianbo Mao, Guangwu Tang, Yongfeng Du
The degradation of bridge structural performance arises from the combined influence of various factors. Performance assessment and reliable prediction of bridge performance degradation through effective utilizing of detection information updates is a challenging problem. In this paper, the concept of performance indicators is redefined, employing to delineate bridge performance degradation. A bridge performance degradation model (the error ≤8%) is formulated, considering the multiple-variable Bayesian dynamic linear method (MBDLM) and revealing the coupling mechanisms among factors influencing bridge performance degradation. On this basis, the prediction performance of the model is quantitatively evaluated by three metrics: mean squared error, predictive mean squared error and mean absolute percentage error. A methodology is presented for the assessment, prediction, and maintenance reinforcement of in-service bridge structural performance degradation. This approach holds promise for future applications in safety assessments and the decision-making process for preventive maintenance of operational bridges.
{"title":"Bridge performance degradation model based on the multi-variate bayesian dynamic linear method","authors":"Guojun Yang, Li Tian, Jianbo Mao, Guangwu Tang, Yongfeng Du","doi":"10.1177/13694332241266541","DOIUrl":"https://doi.org/10.1177/13694332241266541","url":null,"abstract":"The degradation of bridge structural performance arises from the combined influence of various factors. Performance assessment and reliable prediction of bridge performance degradation through effective utilizing of detection information updates is a challenging problem. In this paper, the concept of performance indicators is redefined, employing to delineate bridge performance degradation. A bridge performance degradation model (the error ≤8%) is formulated, considering the multiple-variable Bayesian dynamic linear method (MBDLM) and revealing the coupling mechanisms among factors influencing bridge performance degradation. On this basis, the prediction performance of the model is quantitatively evaluated by three metrics: mean squared error, predictive mean squared error and mean absolute percentage error. A methodology is presented for the assessment, prediction, and maintenance reinforcement of in-service bridge structural performance degradation. This approach holds promise for future applications in safety assessments and the decision-making process for preventive maintenance of operational bridges.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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