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Computer Modeling in engineering & Sciences 2024年第8期140卷 2079-2102页

作者： Yujing Ma Zhongwang Wang Jieyuan Zhang Ruijin Huo Xiaohui Yuan College of Architecture and civil engineering Xinyang Normal UniversityXinyang464000China Henan International Joint Laboratory of structural Mechanics and computational Simulation College of Architecture and Civil EngineeringHuanghuai UniversityZhumadian463000China Academy of Military Sciences Beijing100091China Henan New Environmentally-Friendly civil engineering Materials engineering research Center Xinyang Normal UniversityXinyang464000China

In this paper,an adaptive polynomial chaos expansion method(PCE)based on the method of moments(MoM)is proposed to construct surrogate models for electromagnetic scattering and further sensitivity *** MoM is applied to accurately solve the electric field integral equation(EFIE)of electromagnetic scattering from homogeneous dielectric *** the bistatic radar cross section(RCS)as the research object,the adaptive PCE algorithm is devoted to selecting the appropriate order to construct the multivariate surrogate *** corresponding sensitivity results are given by the further derivative operation,which is compared with those of the finite difference method(FDM).Several examples are provided to demonstrate the effectiveness of the proposed algorithm for sensitivity analysis of electromagnetic scattering from homogeneous dielectric targets.

关键词： Adaptive polynomial chaos expansion method method of moments radar cross section electromagnetic scattering

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of structural and civil engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of civil engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of engineering Thammasat UniversityPathumthani 12120Thailand

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 *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** 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 *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

Flexural behavior of textile reinforced mortar-autoclaved lightweight aerated concrete composite panels

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Frontiers of structural and civil engineering 2024年第5期18卷 776-787页

作者： Liying GUO Mingke DENG Wei ZHANG Tong LI Yangxi ZHANG Mengyu CAO Xian HU Department of civil engineering Xi’an University of Architecture and TechnologyXi’an 710055China Key Lab of structural engineering and Earthquake Resistance Ministry of Education(XAUAT)Xi’an 710055China Scene engineering design institute Xi’an 710016China Dahua group Shang hai 200062China

To improve the deficiencies of prefabricated autoclaved lightweight aerated concrete(ALC)panel such as susceptibility to cracking and low load-bearing capacity,a textile-reinforced mortar-autoclaved lightweight aerated concrete(TRM-ALC)composite panel was developed in this *** group of reference ALC panels and five groups of TRM-ALC panels were fabricated and subjected to four-point flexural *** was applied on the tensile side of the ALC panels to create *** variable parameters were the plies of textile(one or two),type of textile(basalt or carbon),and whether the matrix(without textile)was applied on the compression side of *** results showed that a bonding only 8-mm-thick TRM layer on the surface of the ALC panel could increase the cracking load by 180%−520%.The flexural capacity of the TRM-ALC panel increased as the number of textile layers *** reinforcement of the matrix on the compressive side could further enhance the stiffness and ultimate loadbearing capacity of the TRM-ALC *** panels with basalt textile failed in flexural mode,with the rupture of fabric *** with carbon textile failed in shear mode due to the ultra-high tensile strength of *** addition,analytical models related to the different failure modes were presented to estimate the ultimate load-carrying capacity of the TRM-ALC panels.

关键词： prefabricated autoclaved lightweight aerated concrete panel textile-reinforced mortar cracking load flexural capacity stiffness

Predicting torsional capacity of reinforced concrete members by data-driven machine learning models

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Frontiers of structural and civil engineering 2024年第3期18卷 444-460页

作者： Shenggang CHEN Congcong CHEN Shengyuan LI Junying GUO Quanquan GUO Chaolai LI School of Mechanics and civil engineering China University of Mining and TechnologyXuzhou 211116China Jiangsu Key Laboratory Environmental Impact and structural Safety in engineering China University of Mining and TechnologyXuzhou 211116China China Nuclear Power Design Co.Ltd. Shenzhen 518031China School of transportation and science engineering Beihang UniversityBeijing 100191China China Construction First group Corporation Limited Beijing 100161China

Due to the complicated three-dimensional behaviors and testing limitations of reinforced concrete(RC)members in torsion,torsional mechanism exploration and torsional performance prediction have always been *** the present paper,several machine learning models were applied to predict the torsional capacity of RC *** results of a total of 287 torsional specimens were collected through an overall literature *** of extreme gradient boosting machine(XGBM),random forest regression,back propagation artificial neural network and support vector machine,were trained and tested by 10-fold cross-validation *** performances of proposed machine learning models were evaluated and compared,both with each other and with the calculated results of existing design codes,i.e.,GB 50010,ACI 318-19,and Eurocode *** results demonstrated that better predictive performance was achieved by machine learning models,whereas GB 50010 slightly overestimated the torsional capacity,and ACI 318-19 and Eurocode 2 underestimated it,especially in the case of ACI *** XGBM model gave the most favorable predictions with R^(2)=0.999,RMSE=1.386,MAE=0.86,andλ=***,strength of concrete was the most sensitive input parameters affecting the reliability of the predictive model,followed by transverse-to-longitudinal reinforcement ratio and total reinforcement ratio.

关键词： RC members torsional capacity machine learning models design codes

Calculation method for the formation time of dynamic filter cake in slurry shield tunneling

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Frontiers of structural and civil engineering 2024年第9期18卷 1337-1349页

作者： Yinzun YANG Dajun YUAN Changyan DU Dalong JIN Jun HAO Key Laboratory of the Ministry of Education for Urban Underground engineering Beijing Jiaotong UniversityBeijing 100044China School of civil engineering Beijing Jiaotong UniversityBeijing 100044China China Railway 14th Bureau group Mega Shield engineering Co. Ltd.Nanjing 211800China

In slurry shield tunneling,the stability of tunnel face is closely related to the filter *** cutting of the cutterhead has negative impact on the formation of filter *** study focuses on the formation time of dynamic filter cake considering the filtration effect and rotation of *** effect is the key factor for slurry infiltration.A multilayer slurry infiltration experiment system is designed to investigate the variation of filtrate rheological property in infiltration *** mass concentration C_(L),soil permeability coefficient k,the particle diameter ratio between soil equivalent grain size and representative diameter of slurry particles d_(10)/D_(85) are selected as independent design variables to fit the computational formula of filtration *** on the relative relation between the mass of deposited particles in soil pores and infiltration time,a mathematical model for calculating the formation time of dynamic filter cake is proposed by combining the formation criteria and formation rate of external filter *** accuracy of the proposed model is verified through existing experiment *** results show that filtration coefficient is positively correlated with slurry mass concentration,while negatively correlated with the soil permeability coefficient and the particle diameter ratio between soil and *** infiltration distance increases,the adsorption capacity of soil skeleton to slurry particles gradually *** formation time of external filter cake is significantly lower than internal filter cake and the ratio is approximately *** the dynamic cutting of the cutterhead,the formation time is positively associated with the rotation speed of cutter head,while negatively with the phase angle difference between adjacent cutter *** formation rate of external filter cake is greater than 98%when d_(10)/D_(85)≤*** increasing the content or decreasing the diameter size of solid-phase

关键词： slurry infiltration filtration coefficient dynamic filter cake formation time rotation speed phase angle

Geometric quality evaluation of three-dimensional printable concrete using computational fluid dynamics

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Frontiers of structural and civil engineering 2024年第7期18卷 963-976页

作者： Weijiu CUI Haijun SUN Jiangang ZHOU Sheng WANG Xinyu SHI Yaxin TAO Department of civil engineering Qingdao University of TechnologyQingdao 266000China Innovation Institute for Sustainable Maritime Architecture research and Technology Qingdao University of TechnologyQingdao 266000China Intelligent Construction Lab Qingdao University of TechnologyQingdao 266000China Shandong Zhaojin Industrial Development Co. Ltd.Yantai 264000China Architectural engineering Institute Weifang Engineering Vocational CollegeWeifang 261000China Qingjian group Co. Ltd.Qingdao 266000China College of Architecture and Urban Planning Qingdao University of TechnologyQingdao 266000China Department of structural engineering and Building Materials Ghent UniversityGhent 9000Belgium Institute of Building Materials ETH ZurichZurich 8064Switzerland

The importance of geometrical control of three dimensional(3D)printable concrete without the support of formwork is widely *** this study,a numerical model based on computational fluid dynamics was developed to evaluate the geometrical quality of a 3D printed *** numerical results were compared,using image analysis,with physical cross-sectional sawn *** influence of printing parameters(printing speed,nozzle height,and nozzle diameter)and the rheological behavior of printed materials(yield stress),on the geometrical quality of one printed layer was *** addition,the yield zone of the printed layer was analyzed,giving insights on the critical factors for geometrical control in 3D concrete *** indicated that the developed model can precisely describe the extrusion process,as well as the cross-sectional quality.

关键词： digital fabrication 3D concrete printing geometric quality computational fluid dynamics printing parameters yield stress

An Optimization Method for Seismic Reliability of Regional Road Networks

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Journal of Traffic and Transportation engineering(English Edition) 2024年

作者： Fei Wang Minqing Zhu Hongjun Cui Qihang Zhou Xinyu Sun Di Zhou Xinwei Ma School of Architecture and Art Design， Hebei University of Technology School of civil and Transportation engineering， Hebei University of Technology Tianjin Sino-German University of applied Sciences Hebei Expressway group Limited， Shi Huang Branch

To enhance the efficacy of seismic reliability assessments within regional road networks， this study introduces the constructs of Forbidden Edge Sets and Iterable Edge Sets within the framework of the Quick Fixed-node Unconnected Subgraph Algorithm (QFUS). Through optimization of the subgraph iteration process， the Quick Subgraph Algorithm (QSA) has been developed， exhibiting a diminished temporal complexity. Examining the topological architecture of road network systems， this research proposes the notion of road unit criticality. The corresponding equations have been formulated and empirically validated. Addressing the seismic optimization challenge of road networks， an optimization model has been constructed， and the Additive Edge Method has been introduced， aiming to minimize the requisite investment for enhancements. The algorithms QSA and the Additive Edge Method were applied to the road network in the vicinity of Zhangjiakou for empirical analysis. The results indicate that the QSA algorithm enhances the computational efficiency of network reliability by 33.04% in comparison to the QFUS algorithm. The metric of road unit criticality effectively quantifies the influence of various road unit reinforcement strategies on the seismic reliability enhancement of road network systems. The Additive Edge Method proposed herein is shown to minimize the analytical scope required for road units within the network and achieves cost minimization for seismic optimization of the road network. The scientific validity and logical soundness of the Additive Edge Method have been corroborated through the application of the control variable technique.

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Hierarchical model updating for high-speed maglev vehicle/guideway coupled system based on multiobjective optimization

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Frontiers of structural and civil engineering 2024年第5期18卷 788-804页

作者： Dexiang Li Jingyu Huang College of civil engineering Tongji UniversityShanghai 200092China National Maglev Transportation engineering R&D Center Tongji UniversityShanghai 201804China

The high-speed maglev vehicle/guideway coupled model is an essential simulation tool for investigating vehicle dynamics and mitigating coupled *** improve its accuracy efficiently,this study investigated a hierarchical model updating method integrated with field ***,a high-speed maglev vehicle/guideway coupled model,taking into account the real effect of guideway material properties and elastic restraint of bearings,was developed by integrating the finite element method,multi-body dynamics,and electromagnetic levitation ***,simultaneous in-site measurements of the vehicle/guideway were conducted on a high-speed maglev test line to analyze the system response and structural modal *** the hierarchical updating,an Elman neural network with the optimal Latin hypercube sampling method was used to substitute the FE guideway model,thus improving the computational *** multi-objective particle swarm optimization algorithm with the gray relational projection method was applied to hierarchically update the parameters of the guideway layer and magnetic force layer based on the measured modal parameters and the electromagnet vibration,***,the updated coupled model was compared with the field measurements,and the results demonstrated the model’s accuracy in simulating the actual dynamic response,validating the effectiveness of the updating method.

关键词： high-speed maglev vehicle/guideway coupled model field measurement model updating neural network multi-objective optimization

Acoustic emission characteristics of damage evolution of multi-scale fiber reinforced rubberized concrete under uniaxial compression and tension after being subjected to high temperatures

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Frontiers of structural and civil engineering 2024年第8期18卷 1237-1266页

作者： Shaoqi ZHANG Yao ZHANG Qianru LEI Yumeng YANG Yichao WANG Fei XU Zhiguo YAN Hehua ZHU Laboratory of Fire Safety School of Safety Engineering and Emergency ManagementShijiazhuang Tiedao UniversityShijiazhuang 050043China structural Health Monitoring and Control Key Laboratory of Hebei Province Shijiazhuang Tiedao UniversityShijiazhuang 050043China Department of Geotechnical engineering Tongji UniversityShanghai 200092China

Recently developed multi-scale fiber(i.e.,CaCO3 whisker,polyvinyl alcohol(PVA)fiber,and steel fiber)reinforced rubberized concrete exhibits excellent mechanical properties and spalling resistance at high *** of macro properties such as strength and Young’s modulus cannot reveal and characterize damage mechanisms,particularly those relating to the multi-scale fiber strengthening *** this study,acoustic emission(AE)technology is applied to investigate the impact of multi-scale fiber on the damage evolution of rubberized concrete exposed to high temperatures,under the uniaxial compression and tension loading *** mechanical properties,AE event location,peak frequency,b-value,the ratio of rise time to amplitude(RA),average frequency(AF)values,and AE energy of specimens are *** results show that the number of events observed using AE gradually increases as the loading *** crumb rubber and fibers inhibit the generation and development of the *** is concluded that both the peak frequency and b-value reflect the extension process of *** the cracks develop from the micro scale to the macro scale,the peak frequency tends to be distributed in a lower frequency range,and the b-value decreases *** the peak stress point,the AE energy increases rapidly and the b-value *** specimens without multi-scale fibers exhibit brittle failure,while the specimens with fibers exhibit ductile *** addition,adding multi-scale fibers and crumb rubber increases the peak frequency in the medium and high frequency ranges,indicating a positive effect on inhibiting crack *** being subjected to high temperatures,the maximum and minimum b-values decrease,reflecting an increase in the number of initial cracks due to thermal ***,the RA and AF values are used to classify tensile and shear *** specimens fracture with more shear cracks under compression,and there are more tensile

关键词： multi-scale fibers acoustic emission fracture damage evolution rubberized concrete

Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning

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Frontiers of structural and civil engineering 2024年第4期18卷 516-535页

作者： Than V.TRAN H.NGUYEN-XUAN Xiaoying ZHUANG Institute of Photonics Leibniz University HannoverHannover 30167Germany CIRTech Institute HUTECH UniversityHo Chi Minh City 700000Vietnam College of civil engineering Tongji UniversityShanghai 200092China

Identifying crack and predicting crack propagation are critical processes for the risk assessment of engineering *** traditional approaches to crack modeling are faced with issues of high computational costs and excessive computing *** address this issue,we explore the potential of deep learning(DL)to increase the efficiency of crack detection and forecasting crack ***,there is no single algorithm that can fit all data sets well or can apply in all cases since specific tasks *** the paper,we present DL models for identifying cracks,especially on concrete surface images,and for predicting crack ***,SegNet and U-Net networks are used to identify concrete *** gradient descent(SGD)and adaptive moment estimation(Adam)algorithms are applied to minimize loss function during ***,time series algorithms including gated recurrent unit(GRU)and long short-term memory(LSTM)are used to predict crack *** experimental findings indicate that the U-Net is more robust and efficient than the SegNet for identifying crack segmentation and achieves the most outstanding *** evaluation of crack propagation,GRU and LSTM are used as DL models and results show good agreement with the experimental data.

关键词： deep learning crack segmentation crack propagation encoder−decoder recurrent neural network