Many multi-story or highrise buildings consisting of a number of identical stories are usually considered as periodic spring-mass systems. The general expressions of natural frequencies, mode shapes, slopes and curvat...Many multi-story or highrise buildings consisting of a number of identical stories are usually considered as periodic spring-mass systems. The general expressions of natural frequencies, mode shapes, slopes and curvatures of mode shapes of the periodic spring-mass system by utilizing the periodic structure theory are derived in this paper. The sensitivities of these mode parameters with respect to structural damages, which do not depend on the physical parameters of the original structures, are obtained. Based on the sensitivity analysis of these mode parameters, a two-stage method is proposed to localize and quantify damages of multi-story or highrise buildings. The slopes and curvatures of mode shapes, which are highly sensitive to local damages, are used to localize the damages. Subsequently, the limited measured natural frequencies, which have a better accuracy than the other mode parameters, are used to quantify the extent of damages within the potential damaged locations. The experimental results of a 3-story experimental building demonstrate that the single or multiple damages of buildings, either slight or severe, can be correctly localized by using only the slope or curvature of mode shape in one of the lower modes, in which the change of natural frequency is the largest, and can be accurately quantified by the limited measured natural frequencies with noise pollution.展开更多
A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized fle...A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized flexibility matrix (GFM) proposed to solve the damage identification problem is recalled and a modal expansion method is introduced. Next, the objective function for iterative optimization process based on the GFM is formulated, and the Trust-Region algorithm is utilized to obtain the solution of the optimization problem for multiple damage cases. And then for computing the objective function gradient, the sensitivity analysis regarding design variables is derived. In addition, due to the spatial incompleteness, the influence of stiffness reduction and incomplete modal measurement data is discussed by means of two numerical examples with several damage cases. Finally, based on the computational results, it is evident that the presented approach provides good validity and reliability for the large and complicated engineering structures.展开更多
A hybrid numerical-experimental approach to identify elastic modulus of a textile composite panel using vibration test data is proposed and investi- gated. Homogenization method is adopted to predict the initial value...A hybrid numerical-experimental approach to identify elastic modulus of a textile composite panel using vibration test data is proposed and investi- gated. Homogenization method is adopted to predict the initial values of elastic parameters of the composite, and parameter identification is transformed to an optimization problem in which the objective function is the minimization of the discrepancies between the experimental and numerical modal data. Case study is conducted employing a woven fabric reinforced composite panel. Three parameters （Ell, E22, G12） with higher sensitivities are selected to be identified. It is shown that the elastic parameters can be accurately identified from experimental modal data.展开更多
The accurate mathematical models for complicated structures are verydifficult to construct.The work presented here provides an identification method for estimating the mass, damping , and stiffness matrices of linear ...The accurate mathematical models for complicated structures are verydifficult to construct.The work presented here provides an identification method for estimating the mass, damping , and stiffness matrices of linear dynamical systems from incompleteexperimental data. The mass, stiffness, and damping matrices are assumed to be real,symmetric, and positive definite. The partial set of experimental complex eigenvalues and corresponding eigenvectors are given. In the proposed method the least squaresalgorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters. several illustrative examples, are presented to demonstrate the reliability of the proposed method .It is emphasized thatthe mass, damping and stiffness martices can be identified simultaneously.展开更多
The accurate mathematical models for complicated structures are very difficult to construct.The work presented here provides an identification method for estimating the mass.damping,and stiffness matrices of linear dy...The accurate mathematical models for complicated structures are very difficult to construct.The work presented here provides an identification method for estimating the mass.damping,and stiffness matrices of linear dynamical systems from incomplete experimental data.The mass,stiffness and damping matrices are assumed to be real,symmetric,and positive definite The partial set of experimental complex eigenvalues and corresponding eigenvectors are given.In the proposed method the least squares algorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters.Seeveral illustative examples,are presented to demonstrate the reliability of the proposed method .It is emphasized that the mass,damping and stiffness matrices can be identified simultaneously.展开更多
Takeover safety draws increasing attention in the intelligent transportation as the new energy vehicles with cutting-edge autopilot capabilities vigorously blossom on the road.Despite recent studies highlighting the i...Takeover safety draws increasing attention in the intelligent transportation as the new energy vehicles with cutting-edge autopilot capabilities vigorously blossom on the road.Despite recent studies highlighting the importance of drivers’emotions in takeover safety,the lack of emotion-aware takeover datasets hinders further investigation,thereby constraining potential applications in this field.To this end,we introduce ViE-Take,the first Vision-driven(Vision is used since it constitutes the most cost-effective and user-friendly solution for commercial driver monitor systems)dataset for exploring the Emotional landscape in Takeovers of autonomous driving.ViE-Take enables a comprehensive exploration of the impact of emotions on drivers’takeover performance through 3 key attributes:multi-source emotion elicitation,multi-modal driver data collection,and multi-dimensional emotion annotations.To aid the use of ViE-Take,we provide 4 deep models(corresponding to 4 prevalent learning strategies)for predicting 3 different aspects of drivers’takeover performance(readiness,reaction time,and quality).These models offer benefits for various downstream tasks,such as driver emotion recognition and regulation for automobile manufacturers.Initial analysis and experiments conducted on ViE-Take indicate that(a)emotions have diverse impacts on takeover performance,some of which are counterintuitive;(b)highly expressive social media clips,despite their brevity,prove effective in eliciting emotions(a foundation for emotion regulation);and(c)predicting takeover performance solely through deep learning on vision data not only is feasible but also holds great potential.展开更多
Polymer property prediction is a critical task in polymer science.Conventional approaches typically rely on a single data modality or a limited set of modalities,which constrains both predictive accuracy and practical...Polymer property prediction is a critical task in polymer science.Conventional approaches typically rely on a single data modality or a limited set of modalities,which constrains both predictive accuracy and practical applicability.In this paper,we present Uni-Poly,a novel framework that integrates diverse data modalities to achieve a comprehensive and unified representation of polymers.Uni-Poly encompasses all commonly used structural formats,including SMILES,2D graphs,3D geometries,and fingerprints.In addition,it incorporates domain-specific textual descriptions to enrich the representation.Experimental results demonstrate that Uni-Poly outperforms all single-modality and multi-modality baselines across various property prediction tasks.The integration of textual descriptions provides complementary information that structural representations alone cannot capture.These findings underscore the value of leveraging multimodal and domain-specific information to enhance polymer property prediction,thereby advancing high-throughput screening and the discovery of novel polymer materials.展开更多
The liver is the second-largest organ in the human body and is essential for digesting food and removing toxic substances.Viruses,obesity,alcohol use,and other factors can damage the liver and cause liver disease.The ...The liver is the second-largest organ in the human body and is essential for digesting food and removing toxic substances.Viruses,obesity,alcohol use,and other factors can damage the liver and cause liver disease.The diagnosis of liver disease used to depend on the clinical experience of doctors,which made it subjective,difficult,and time-consuming.Deep learning has made breakthroughs in various fields;thus,there is a growing interest in using deep learning methods to solve problems in liver research to assist doctors in diagnosis and treatment.In this paper,we provide an overview of deep learning in liver research using 139 papers from the last 5 years.We also show the relationship between data modalities,liver topics,and applications in liver research using Sankey diagrams and summarize the deep learning methods used for each liver topic,in addition to the relations and trends between these methods.Finally,we discuss the challenges of and expectations for deep learning in liver research.展开更多
Condition assessment of bridges has become increasingly important. In order to accurately simulate the real bridge, finite element （FE） model updating method is often applied. This paper presents the calibration of ...Condition assessment of bridges has become increasingly important. In order to accurately simulate the real bridge, finite element （FE） model updating method is often applied. This paper presents the calibration of the FE model of a reinforced concrete tied-arch bridge using Douglas-Reid method in combination with Rosenbrock optimization algorithm. Based on original drawings and topographic survey, a FE model of the investigated bridge is created. Eight global modes of vibration of the bridge are identified by ambient vibration tests and the frequency domain decomposition technique. Then, eight structural parameters are selected for FE model updating procedure through sensitivity analysis. Finally, the optimal structural parameters are identified using Rosenbrock optimization algorithm. Results show that although the identified parameters lead to a perfect agreement between approximate and measured natural frequencies, they may not be the optimal variables which minimize the differences between numerical and experimental modal data. However, a satisfied agreement between them is still presented. Hence, FE model updating based on Douglas-Reid method and Rosenbrock optimization algorithm could be used as an alternative to other complex updating procedures.展开更多
The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this...The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming loop.The surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement devices.The proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential scenarios.To achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training process.The approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical examples.The study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured data.Overall,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally intensive.This approach also shows potential for broader applications in structural damage detection.展开更多
基金Project supported by the National Natural Science Foundation of China (No. 50378041) Specialized Research Fund for Doctoral Programs of Higher Education (No. 20030487016).
文摘Many multi-story or highrise buildings consisting of a number of identical stories are usually considered as periodic spring-mass systems. The general expressions of natural frequencies, mode shapes, slopes and curvatures of mode shapes of the periodic spring-mass system by utilizing the periodic structure theory are derived in this paper. The sensitivities of these mode parameters with respect to structural damages, which do not depend on the physical parameters of the original structures, are obtained. Based on the sensitivity analysis of these mode parameters, a two-stage method is proposed to localize and quantify damages of multi-story or highrise buildings. The slopes and curvatures of mode shapes, which are highly sensitive to local damages, are used to localize the damages. Subsequently, the limited measured natural frequencies, which have a better accuracy than the other mode parameters, are used to quantify the extent of damages within the potential damaged locations. The experimental results of a 3-story experimental building demonstrate that the single or multiple damages of buildings, either slight or severe, can be correctly localized by using only the slope or curvature of mode shape in one of the lower modes, in which the change of natural frequency is the largest, and can be accurately quantified by the limited measured natural frequencies with noise pollution.
文摘A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized flexibility matrix (GFM) proposed to solve the damage identification problem is recalled and a modal expansion method is introduced. Next, the objective function for iterative optimization process based on the GFM is formulated, and the Trust-Region algorithm is utilized to obtain the solution of the optimization problem for multiple damage cases. And then for computing the objective function gradient, the sensitivity analysis regarding design variables is derived. In addition, due to the spatial incompleteness, the influence of stiffness reduction and incomplete modal measurement data is discussed by means of two numerical examples with several damage cases. Finally, based on the computational results, it is evident that the presented approach provides good validity and reliability for the large and complicated engineering structures.
基金supported by the Program for New Century Excellent Talents in University(NCET11-0086)the National Natural Science Foundation of China(10902024)+1 种基金the Doctoral Program of Higher Education of China(20130092120039)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD-1105007001)
文摘A hybrid numerical-experimental approach to identify elastic modulus of a textile composite panel using vibration test data is proposed and investi- gated. Homogenization method is adopted to predict the initial values of elastic parameters of the composite, and parameter identification is transformed to an optimization problem in which the objective function is the minimization of the discrepancies between the experimental and numerical modal data. Case study is conducted employing a woven fabric reinforced composite panel. Three parameters （Ell, E22, G12） with higher sensitivities are selected to be identified. It is shown that the elastic parameters can be accurately identified from experimental modal data.
文摘The accurate mathematical models for complicated structures are verydifficult to construct.The work presented here provides an identification method for estimating the mass, damping , and stiffness matrices of linear dynamical systems from incompleteexperimental data. The mass, stiffness, and damping matrices are assumed to be real,symmetric, and positive definite. The partial set of experimental complex eigenvalues and corresponding eigenvectors are given. In the proposed method the least squaresalgorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters. several illustrative examples, are presented to demonstrate the reliability of the proposed method .It is emphasized thatthe mass, damping and stiffness martices can be identified simultaneously.
文摘The accurate mathematical models for complicated structures are very difficult to construct.The work presented here provides an identification method for estimating the mass.damping,and stiffness matrices of linear dynamical systems from incomplete experimental data.The mass,stiffness and damping matrices are assumed to be real,symmetric,and positive definite The partial set of experimental complex eigenvalues and corresponding eigenvectors are given.In the proposed method the least squares algorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters.Seeveral illustative examples,are presented to demonstrate the reliability of the proposed method .It is emphasized that the mass,damping and stiffness matrices can be identified simultaneously.
基金supported by the National Natural Science Foundation of China(no.62072153)the Anhui Provincial Key Technologies R&D Program(no.2022h11020015)the 111 Center(no.B14025).
文摘Takeover safety draws increasing attention in the intelligent transportation as the new energy vehicles with cutting-edge autopilot capabilities vigorously blossom on the road.Despite recent studies highlighting the importance of drivers’emotions in takeover safety,the lack of emotion-aware takeover datasets hinders further investigation,thereby constraining potential applications in this field.To this end,we introduce ViE-Take,the first Vision-driven(Vision is used since it constitutes the most cost-effective and user-friendly solution for commercial driver monitor systems)dataset for exploring the Emotional landscape in Takeovers of autonomous driving.ViE-Take enables a comprehensive exploration of the impact of emotions on drivers’takeover performance through 3 key attributes:multi-source emotion elicitation,multi-modal driver data collection,and multi-dimensional emotion annotations.To aid the use of ViE-Take,we provide 4 deep models(corresponding to 4 prevalent learning strategies)for predicting 3 different aspects of drivers’takeover performance(readiness,reaction time,and quality).These models offer benefits for various downstream tasks,such as driver emotion recognition and regulation for automobile manufacturers.Initial analysis and experiments conducted on ViE-Take indicate that(a)emotions have diverse impacts on takeover performance,some of which are counterintuitive;(b)highly expressive social media clips,despite their brevity,prove effective in eliciting emotions(a foundation for emotion regulation);and(c)predicting takeover performance solely through deep learning on vision data not only is feasible but also holds great potential.
基金funded by the National Natural Science Foundation of China,No.62474183,The funder played no role in study design,data collection,analysis and interpretation of data,or the writing of this manuscript.
文摘Polymer property prediction is a critical task in polymer science.Conventional approaches typically rely on a single data modality or a limited set of modalities,which constrains both predictive accuracy and practical applicability.In this paper,we present Uni-Poly,a novel framework that integrates diverse data modalities to achieve a comprehensive and unified representation of polymers.Uni-Poly encompasses all commonly used structural formats,including SMILES,2D graphs,3D geometries,and fingerprints.In addition,it incorporates domain-specific textual descriptions to enrich the representation.Experimental results demonstrate that Uni-Poly outperforms all single-modality and multi-modality baselines across various property prediction tasks.The integration of textual descriptions provides complementary information that structural representations alone cannot capture.These findings underscore the value of leveraging multimodal and domain-specific information to enhance polymer property prediction,thereby advancing high-throughput screening and the discovery of novel polymer materials.
基金supported by grants from the National Natural Science Foundation of China(Nos.12071458 and 71731009).
文摘The liver is the second-largest organ in the human body and is essential for digesting food and removing toxic substances.Viruses,obesity,alcohol use,and other factors can damage the liver and cause liver disease.The diagnosis of liver disease used to depend on the clinical experience of doctors,which made it subjective,difficult,and time-consuming.Deep learning has made breakthroughs in various fields;thus,there is a growing interest in using deep learning methods to solve problems in liver research to assist doctors in diagnosis and treatment.In this paper,we provide an overview of deep learning in liver research using 139 papers from the last 5 years.We also show the relationship between data modalities,liver topics,and applications in liver research using Sankey diagrams and summarize the deep learning methods used for each liver topic,in addition to the relations and trends between these methods.Finally,we discuss the challenges of and expectations for deep learning in liver research.
文摘Condition assessment of bridges has become increasingly important. In order to accurately simulate the real bridge, finite element （FE） model updating method is often applied. This paper presents the calibration of the FE model of a reinforced concrete tied-arch bridge using Douglas-Reid method in combination with Rosenbrock optimization algorithm. Based on original drawings and topographic survey, a FE model of the investigated bridge is created. Eight global modes of vibration of the bridge are identified by ambient vibration tests and the frequency domain decomposition technique. Then, eight structural parameters are selected for FE model updating procedure through sensitivity analysis. Finally, the optimal structural parameters are identified using Rosenbrock optimization algorithm. Results show that although the identified parameters lead to a perfect agreement between approximate and measured natural frequencies, they may not be the optimal variables which minimize the differences between numerical and experimental modal data. However, a satisfied agreement between them is still presented. Hence, FE model updating based on Douglas-Reid method and Rosenbrock optimization algorithm could be used as an alternative to other complex updating procedures.
基金This study was supported by Bualuang ASEAN Chair Professor Fund.
文摘The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming loop.The surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement devices.The proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential scenarios.To achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training process.The approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical examples.The study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured data.Overall,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally intensive.This approach also shows potential for broader applications in structural damage detection.
