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SSRN 2024年

作者： Zhang, Mingming Zhu, Lihua Gao, Shan Yuan, Hanquan Liu, Tong School of Civil Engineering Xi’an University of Architecture and Technology Shaanxi Xi’an710055 China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China

This paper focuses on energy harvesting and smart controls in concrete structures using both experimental and theoretical approaches. The growing demand for sustainable energy solutions has led to innovations in materials like concrete, which offer energy conversion and efficient control possibilities. This study introduces a novel concept for concrete structures using piezoelectric nanoparticles, specifically zinc oxide nanoparticles (ZnO), offering three key benefits: (a) enhanced strength, (b) energy harvesting and storage, and (c) smart control. The experimental phase involves modifying concrete structures with energy harvesting technologies. Dispersion of nanoparticles in water is a key challenge, addressed through magnetic stirring, ultrasonic homogenization, and mechanical stirring. Multiple concrete specimens with nanoparticles were produced, followed by tests including compressive strength assessments, energy harvesting under mechanical loads, and intelligent control of concrete deflection through voltage application. On the theoretical front, modeling and numerical methods predict the behavior of energy harvesting concrete structures. The precise five-parameter theory, accounting for Poisson's effect, is employed for modeling. After deriving governing equations, differential quadrature numerical technique computes deflection and output voltage. Comparisons and validations are made between experimental and simulation results. The experiments reveal that the optimal ZnO nanoparticle content is 0.9%, leading to significant compressive strength improvements of 22.14%, 27.31%, and 34.28% for 7, 14, and 28-day samples, respectively, compared to controls. Beyond this content, compressive strength diminishes. Moreover, 0.9% ZnO-concrete produces an output voltage of 32 volts, well-justified technically and economically. Additionally, +50V voltage induces 9.2mm deformation, while -50V voltage yields 5.8mm, showcasing a substantial 63% deformation alteration potenti

关键词： Energy harvesting

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Development and Application of an Automated On-Site Measuring Robot for Post-Construction Quality control

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SSRN 2024年

作者： Wu, Hangzi Yang, Yan China Construction Eighth Engineering Division Co. Ltd. Shanghai200112 China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China

Traditional manual methods for on-site quality measurement are often inefficient. Existing robots, typically combining a robot dog with 3D laser scanners, encounter challenges like significant storage requirements, dependence on specialized post-processing software, and high costs. Robots with multiple sensors may face limitations in handling diverse measurement items. Therefore, this paper produces a cost-effective and fully automated on-site measuring robot. In comparison to commercial 3D laser scanners, the hardware system's cost is reduced by 30%, and the data storage space sees a 90% reduction by retaining effective point cloud data during the processing of data. The robot is proficient in measuring dimensions, wall evenness, alignments, floor height, and internal and external corner *** experiments confirm the reliability of the measured data, displaying minimal discrepancies compared to manual measurements. In practical applications, the robot demonstrates a 40% efficiency improvement over manual measurements, resulting in a minimum 50% reduction in lab.r costs. © 2024, The Authors. All rights reserved.

关键词： Robots

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dynamic shear responses of RC circular columns strengthened with recycled FRPs

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structures 2025年 78卷

作者： Yan, Zhi-Wei Song, Yanchen Bai, Yu-Lei Liu, Kun Faculty of Civil Engineering and Mechanics Jiangsu University Zhenjiang212013 China State Key Laboratory of Bridge Engineering Safety and Resilience Beijing University of Technology Beijing100124 China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China

Increasingly heavy urban traffic exacerbates vehicle impacts on reinforced concrete (RC) bridge columns, possibly leading to shear failure and structural collapse. To enhance the dynamic shear resistance of these columns, fiber reinforced polymers (FRPs) made from recycled polyethylene terephthalate (PET) plastic bottles were used for external strengthening. PET FRPs offer an eco-friendly alternative by repurposing plastic waste, reducing pollution, and promoting circular economy. This study focused on the dynamic shear responses of RC columns strengthened with PET FRPs. The influences of FRP types and number of FRP layers were experimentally revealed in terms of the failure modes, impact force, reaction force, shear force and impact force-displacement relationship. It was found that significant shear damage occurred in RC columns under horizontal impact loads, with carbon FRPs (CFRPs) strengthening unable to alter the failure mode. Nevertheless, the use of PET FRPs transformed the failure mode to a flexural-shear mode, significantly reducing the impact duration, maximum and residual displacements, and energy dissipation. Moreover, a finite element model was validated to describe the impact resistance of PET FRP-strengthened RC columns, as supported by experimental data. A parametric study further investigated the effects of impact velocity, impact mass, number of FRP layers, and reinforcement ratios on the dynamic shear responses. Based on the truss-arch model, a dynamic shear strength model was proposed and can well predict the dynamic shear capacity of FRP-strengthened RC circular columns. © 2025

关键词： Carbon fiber reinforced plastics

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Multi-Level Fusion Method Based on Improved Stacked Hourglass Network for Edge Detection

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SSRN 2024年

作者： Li, Shuang Chen, Yicheng Li, Changqing Feng, Chang Zhai, Changhai Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters The Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China

Edge detection is one of the most challenging tasks in computer vision due to the complexity of various objects and backgrounds. Previous efforts in edge detection often faced significant difficulties, resulting in many fine details being overlooked and coarser edges being detected. This study adopts a stacked hourglass network as the backbone of the architecture, and extracts edge information at different receptive fields using convolution kernels of varying sizes. In the data fusion segment, the CBAM (Convolutional Block Attention Module) attention mechanism is employed to integrate five distinct types of captured edge information. The method was trained on the BIPED (Barcelona Images for Perceptual Edge Detection) dataset, and the results show that it achieved favorable outcomes on the ODS, OIS, and AP metrics in the test sets. Furthermore, the method also demonstrated great predictive performance on UDED (Unified Dataset for Edge Detection) dataset, delivering clearer boundary features. © 2024, The Authors. All rights reserved.

关键词： Data fusion

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Probabilistic Prediction of Post-Earthquake Rubble Risk Distribution Range for Bottom Frame structures

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SSRN 2024年

作者： He, Yuhan Zhai, Changhai Wen, Weiping Key Lab of Structures Dynamic Behavior and Control The Ministry of Education Harbin Institute of Technology Harbin150090 China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disaster The Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China

The rubble generated by the massive collapse of street-front buildings after an earthquake can seriously block roads, affecting the connectivity of the road network in a city and causing serious damage. In order to achieve the needs of urban planning, buildings facing the street are mainly bottom frame structures that cater for both commercial and residential use. However, once an earthquake occurs, the poor seismic performance of the bottom frame structures is quite prone to collapse, which produces a large amount of rubble and brings a great burden to the post-earthquake traffic system. In this paper, based on finite element models for various cases, considering the uncertainty of seismic intensity and height of the bottom frame structure, and combining with the fragility of structural collapse, we propose a method to predict the risk range of the bottom frame structure rubble to quantify the capacity of the transportation system after the earthquake, which can better assess the seismic resilience of the transportation system in the urban area. © 2024, The Authors. All rights reserved.

关键词： Earthquakes

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An active learning method based on Monte Carlo dropout neural network for high-dimensional reliability analysis

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Reliability Engineering and System Safety 2025年 262卷

作者： Sun, Huabin Bao, Yuequan Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China School of Civil Engineering Harbin Institute of Technology Harbin150090 China

In structural reliability analysis, the AK-MCS method, which combines Kriging and Monte Carlo Simulation, is well-acknowledged for its effectiveness but struggles with accuracy and efficiency in high-dimensional and nonlinear scenarios. To leverage the advantages and circumvent the limitations of AK-MCS, an active learning method based on the Monte Carlo dropout (MC-dropout) neural network is proposed. The MC-dropout neural network-based surrogate model provides both predictive mean and standard deviation in complex scenarios with a limited number of samples. By identifying candidate samples and utilizing a learning function that considers predictive mean and standard deviation, the method selects new samples close to the limit state surface with significant uncertainties to update the surrogate model. An ensemble of MC-dropout neural networks is then used to obtain a reliable failure probability. Two convergence criteria are introduced to determine the termination of the active learning process. Two numerical examples, a cantilever beam and an actual cable-stayed bridge are used to demonstrate the efficacy of the proposed method. The results show that the MC-dropout neural network-based surrogate model exhibits adaptivity and flexibility in handling high-dimensional and nonlinear scenarios and the proposed method achieves a relatively accurate failure probability with a limited number of samples. © 2025 Elsevier Ltd

关键词： Failure rate

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Mechanical and Thermal Properties of Environmentally Friendly Straw Boards

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SSRN 2023年

作者： Zheng, Chaorong Sun, Kuo Chen, Yixiao Zhang, Wenyuan Wu, Yue School of Civil Engineering Harbin Institute of Technology Harbin150090 China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China

Straw is an environmentally friendly, cost-effective, and abundant raw material for building boards. To assess the feasibility of using straw boards as alternative sheathing panels in light-steel-members such as walls and floors, the mechanical and thermal properties of straw boards with high densities were investigated. The straw boards involved in this study include five wheat straw strand boards (WSSB12, WSSB15, WSSB18, WSSB25, and WSSB30) and a paper straw board (PSB58). Mechanical properties, including compression, tension, and bending properties, were investigated by tests and a proposed prediction method of tensile strength. In the experimental studies of compression properties, the failure modes and characteristics of load-deformation curves of compressive specimens, and the parameters of compression properties, including the strength, elastic modulus, and Poisson ratio, were analyzed and summarized, and the differences in material directivity were discussed as well. The WSSBs were found to be transverse isotropic material with brittle characteristics in the plane, while the PSB showed similar brittle characteristics in the failure stage. Furthermore, the experimental studies of bending properties and similar analyses and summaries for test results were carried out. Results show that the bending strength is less than the compressive strength for WSSBs, while the case is just the opposite for PSB, and the elastic moduli of bending and compression are close for both WSSBs and PSB. Then, finite element models (FEMs) of the bending test specimens using the user material subroutine UMAT were developed to predict the tensile strength ft, and the prediction results were qualitatively assessed based on the correlation between ft and fb, fc. As verified, the prediction results of ft are believable, and the prediction method proposed in this study is feasible. Such a prediction method could efficiently determine the tensile strength of similar brittle materials withou

关键词： Tensile strength

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A machine learning based solver for pressure Poisson equations

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Theoretical & Applied Mechanics Letters 2022年第5期12卷 315-321页

作者： Ruilin Chen Xiaowei Jin Hui Li Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of TechnologyHarbin 150090China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of TechnologyHarbin 150090China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Harbin Institute of Technology(Shenzhen)Shenzhen 518055China

When using the projection method(or fractional step method)to solve the incompressible Navier-Stokes equations,the projection step involves solving a large-scale pressure Poisson equation(PPE),which is computationally expensive and *** this study,a machine learning based method is proposed to solve the large-scale *** machine learning(ML)-block is used to completely or partially(if not sufficiently accurate)replace the traditional PPE iterative solver thus accelerating the solution of the incompressible Navier-Stokes *** ML-block is designed as a multi-scale graph neural network(GNN)framework,in which the original high-resolution graph corresponds to the discrete grids of the solution domain,graphs with the same resolution are connected by graph convolution operation,and graphs with different resolutions are connected by down/up prolongation *** well trained MLblock will act as a general-purpose PPE solver for a certain kind of flow *** proposed method is verified via solving two-dimensional Kolmogorov flows(Re=1000 and Re=5000)with different source *** the premise of achieving a specified high precision(ML-block partially replaces the traditional iterative solver),the ML-block provides a better initial iteration value for the traditional iterative solver,which greatly reduces the number of iterations of the traditional iterative solver and speeds up the solution of the *** experiments show that the ML-block has great advantages in accelerating the solving of the Navier-Stokes equations while ensuring high accuracy.

关键词： Pressure Poisson equation Machine learning Projection method Multi-scale GNN

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Thermal performance of a novel Trombe wall enhanced by a solar energy focusing approach

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Low-carbon Materials and Green Construction 2024年第1期2卷 379-394页

作者： Xianzhang Dong Huigang Xiao Minglei Ma School of Civil Engineering Harbin Institute of TechnologyHarbin 150090PR China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education (Harbin Institute of Technology)Harbin 150090PR China China Construction Eighth Engineering Bureau Co. LtdShanghaiPR China

The Trombe wall is a passive solar building exterior wall system proposed by Professor Felix Trombe in France,which can collect solar energy to heat buildings without additional energy consumption,making it a focal point of research in building energy ***,its effectiveness is constrained by the low density of solar radiation in winter and the potential for overheating in *** study introduces a novel Trombe wall designed to address these issues through a focused strategy,enabling automatic transition between heating during winter and shading during *** thermal performance parameters of the novel Trombe walls in both winter and summer seasons are examined,and their energy consumption is assessed using experimental research *** indicate that the novel Trombe wall facilitates greater energy savings in both winter and *** compared with traditional Trombe walls,the novel Trombe wall achieves a significant reduction in energy consumption,with up to 55 W/m^(2)in heating load during winter and 47 W/m^(2)in cooling load during *** introduction of this new system holds substantial potential for the realization of zero-energy buildings.

关键词： Trombe wall Thermal performance Focusing approach Overheating

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Interfacial shear behavior in steel-concrete-steel composite structures with stud connectors after freeze-thaw cycles

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Construction and Building Materials 2025年 484卷

作者： Wei, Xudan Yan, Jiachuan Lin, Youzhu Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China School of Civil and Transportation Engineering Northeast Forestry University Harbin150040 China

Steel-concrete-steel (SCS) composite structures have potential applications in the northern cold region. To investigate the interfacial shear performance and failure mechanism of SCS composite structures with stud connectors subjected to freeze-thaw cycles (FTCs), freeze-thaw tests and push-out tests were conducted on 36 specimens to investigate the effects of concrete type, stud quantity, and FTCs. The test results showed that when the core concrete was changed from C30 concrete to ultra-high-performance concrete (UHPC), the shear capacity, shear stiffness, and ductility of the specimens all increased significantly, by approximately two times. Moreover, the failure mode of the specimens shifted remarkably from concrete splitting damage to stud breakage failure. Increasing the stud quantity or FTCs reduced the shear capacity and stiffness of the specimen, yet it exerted a minimal impact on the ductility. In addition, precise finite element models were established to investigate the effect of the relative strength between studs and concrete and the stud spacing on the failure modes and interfacial shear capacity. Based on experimental and finite element data analysis, a formula for calculating the interfacial shear capacity of SCS composite structures after FTCs was proposed. The calculation results were consistent with the test and simulation results, and this formula can serve as a reference for the design and application of SCS composite structures in cold regions. © 2025

关键词： Ultra-high performance concrete

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