查看更多>>摘要：Experimental evaluation is crucial for ensuring the accuracy of real-time hybrid simulation (RTHS) results. While existing methods can calculate time delay and amplitude error, time-varying delays can destabilize RTHS, requiring a method to account for them. This paper proposes using empirical mode decomposition (EMD) to calculate instantaneous control parameters, such as instantaneous amplitude and time delay. Intrinsic mode functions (IMF) from EMD capture the signal's local characteristics at different time scales, and the Hilbert transform is applied to compute these parameters. After EMD, a different number of IMFs may be obtained for calculated displacements than for measured displacements, and this paper gives advice on the IMFs needed to calculate instantaneous control parameters (ICP), and how they should be matched. The signals obtained from the numerical simulation of the benchmark model without and with compensation are firstly subjected to the calculation of ICP, and the results prove the effectiveness of ICP. Subsequently, the predefined displacement test with a multi-degree-of-freedom structure and the RTHS with a single-degree-of-freedom structure and self-centering viscous damper were subjected to the traditional ICP method and the EMD-based ICP calculation method for ICP calculation, respectively, and the comparative results show that the effectiveness of the EMD-based ICP calculation method is increased, and that EMD effectively solves the negative-frequency issue caused by signals with multiple poles between two crossed zeros. These calculations show great potential in improving experimental evaluations.
查看更多>>摘要：Experimental evaluation is crucial for ensuring the accuracy of real-time hybrid simulation (RTHS) results. While existing methods can calculate time delay and amplitude error, time-varying delays can destabilize RTHS, requiring a method to account for them. This paper proposes using empirical mode decomposition (EMD) to calculate instantaneous control parameters, such as instantaneous amplitude and time delay. Intrinsic mode functions (IMF) from EMD capture the signal's local characteristics at different time scales, and the Hilbert transform is applied to compute these parameters. After EMD, a different number of IMFs may be obtained for calculated displacements than for measured displacements, and this paper gives advice on the IMFs needed to calculate instantaneous control parameters (ICP), and how they should be matched. The signals obtained from the numerical simulation of the benchmark model without and with compensation are firstly subjected to the calculation of ICP, and the results prove the effectiveness of ICP. Subsequently, the predefined displacement test with a multi-degree-of-freedom structure and the RTHS with a single-degree-of-freedom structure and self-centering viscous damper were subjected to the traditional ICP method and the EMD-based ICP calculation method for ICP calculation, respectively, and the comparative results show that the effectiveness of the EMD-based ICP calculation method is increased, and that EMD effectively solves the negative-frequency issue caused by signals with multiple poles between two crossed zeros. These calculations show great potential in improving experimental evaluations.
查看更多>>摘要：Global monitoring of structures is vital for assessing their structural integrity, especially with the impact of moving vehicles on railroad bridges. This necessitates simultaneous monitoring of both systems to understand interaction dynamics comprehensively. In vibration-based Structural Health Monitoring fields, demands for directly obtaining displacement responses increase, leading to non-contact sensing adoption. Computer Vision (CV)-based methods, using feature tracking techniques for displacement measurements, have become practical alternatives. The proposed approach utilizes Poor Feature Points, offering global view and overcoming spatial resolution limitations. Addressing challenges related to camera ego-motion in large-scale monitoring, strategies for re-assigning regions of interest based on feature quality are introduced, and camera ego-motion compensated by calibrating feature points. The You Only Look Once algorithm is used for vehicle wheel detection, localizing contact points to examine Vehicle-Bridge Interaction dynamics. A laboratory-scale experiment validation confirms the feasibility of global monitoring with vision sensors, especially in interpreting VBI dynamics.
查看更多>>摘要：Global monitoring of structures is vital for assessing their structural integrity, especially with the impact of moving vehicles on railroad bridges. This necessitates simultaneous monitoring of both systems to understand interaction dynamics comprehensively. In vibration-based Structural Health Monitoring fields, demands for directly obtaining displacement responses increase, leading to non-contact sensing adoption. Computer Vision (CV)-based methods, using feature tracking techniques for displacement measurements, have become practical alternatives. The proposed approach utilizes Poor Feature Points, offering global view and overcoming spatial resolution limitations. Addressing challenges related to camera ego-motion in large-scale monitoring, strategies for re-assigning regions of interest based on feature quality are introduced, and camera ego-motion compensated by calibrating feature points. The You Only Look Once algorithm is used for vehicle wheel detection, localizing contact points to examine Vehicle-Bridge Interaction dynamics. A laboratory-scale experiment validation confirms the feasibility of global monitoring with vision sensors, especially in interpreting VBI dynamics.
查看更多>>摘要：This study introduces a novel carbon nanotube (CNT) cementitious composite sensor developed using pore conductivity theory to address durability and structural compatibility requirements for monitoring ship-bridge collisions in marine environments. The sensor employs a dual-channel sensing mechanism by integrating CNT networks with conductive pathways formed by electrolyte solutions within cement pores. Experimental results demonstrate high sensing accuracy across sensors with varying slenderness ratios, achieving axial and lateral errors under 8%. Notably, sensors with a 1:4 slenderness ratio exhibit significantly enhanced resistance change rates under axial loading, up to 281% within a 10 kN lateral load range. Impact tests further confirm strong correlation between electrical signals and strain gauge measurements when collision speeds range between 1-2 m/s, validating real-time collision damage monitoring capabilities. This research establishes design principles for pore conductivity-based CNT cement sensors while providing theoretical foundations for smart concrete applications in ship-bridge collision monitoring.
查看更多>>摘要：This study introduces a novel carbon nanotube (CNT) cementitious composite sensor developed using pore conductivity theory to address durability and structural compatibility requirements for monitoring ship-bridge collisions in marine environments. The sensor employs a dual-channel sensing mechanism by integrating CNT networks with conductive pathways formed by electrolyte solutions within cement pores. Experimental results demonstrate high sensing accuracy across sensors with varying slenderness ratios, achieving axial and lateral errors under 8%. Notably, sensors with a 1:4 slenderness ratio exhibit significantly enhanced resistance change rates under axial loading, up to 281% within a 10 kN lateral load range. Impact tests further confirm strong correlation between electrical signals and strain gauge measurements when collision speeds range between 1-2 m/s, validating real-time collision damage monitoring capabilities. This research establishes design principles for pore conductivity-based CNT cement sensors while providing theoretical foundations for smart concrete applications in ship-bridge collision monitoring.
Song, JunYan, GongxingAslzadh, F. MirzaGhoniem, Rania M....
221-234页
查看更多>>摘要：A predictive model to determine shear strength and mechanical properties of soil-mix material (soil reinforcement) is required in many geotechnical projects especially when the weight of geomaterial is important for stability or drainage purposes. In this paper, several matching learning (ML) techniques namely Chi-squared Automatic Interaction Detection (CHAID), Classification and Regression Trees (CART), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Generalized Linear Mixed Model (GLMM) were used to predict the effects of reinforcement on cohesion (C) parameter in sandy soil. To establish an appreciate database for prediction purposes, several laboratory tests were planned and conducted on sandy soil mixed with fiber and subsequently, soil properties together with their shear strength parameters were measured. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and then, the mentioned parameters were set as inputs. According to the most effective parameters of predictive ML techniques, many models were constructed to predict C of the soil. The modelling results showed that the CHAID model provides the best prediction performance of cohesion in the short term and long term. Coefficient of determination of one and system error of zero for both train and test stages of CHAID have confirmed that this model is a perfect, powerful and applicable ML technique. The design process and model development presented in this study can be considered and used by the other researchers or engineers in resolving their complicated issues.
Song, JunYan, GongxingAslzadh, F. MirzaGhoniem, Rania M....
221-234页
查看更多>>摘要：A predictive model to determine shear strength and mechanical properties of soil-mix material (soil reinforcement) is required in many geotechnical projects especially when the weight of geomaterial is important for stability or drainage purposes. In this paper, several matching learning (ML) techniques namely Chi-squared Automatic Interaction Detection (CHAID), Classification and Regression Trees (CART), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Generalized Linear Mixed Model (GLMM) were used to predict the effects of reinforcement on cohesion (C) parameter in sandy soil. To establish an appreciate database for prediction purposes, several laboratory tests were planned and conducted on sandy soil mixed with fiber and subsequently, soil properties together with their shear strength parameters were measured. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and then, the mentioned parameters were set as inputs. According to the most effective parameters of predictive ML techniques, many models were constructed to predict C of the soil. The modelling results showed that the CHAID model provides the best prediction performance of cohesion in the short term and long term. Coefficient of determination of one and system error of zero for both train and test stages of CHAID have confirmed that this model is a perfect, powerful and applicable ML technique. The design process and model development presented in this study can be considered and used by the other researchers or engineers in resolving their complicated issues.
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