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A novel porous shock absorption layer for tunnels: Shock absorption performance and parameter analysis

Earthquake Engineering and Engineering Vibration 2025年第2期24卷 437-450页

作者： Zhou Tonglai Dong Changsong Li Shuang Sun Qiangqiang Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of TechnologyHarbin 150090China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of TechnologyHarbin 150090China State Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment CCCC First Highway Consultants Co.Ltd.Xi’an 710075China Institute of Geotechnical Engineering College of Civil Engineering and ArchitectureHebei UniversityBaoding 017002China

A novel porous shock absorption layer is put forward in this study, and the shock absorption performance of the porous shock absorption layer is evaluated based on three-dimensional pseudo-static analysis. The modified reaction acceleration method is adopted and validated in the three-dimensional model. Seven ground motions are selected and the peak ground acceleration is adjusted to 0.2 g, 0.4 g and 0.6 g. The impact of the void ratio and thickness of the porous shock absorption layer is studied, while the surrounding rock grade and tunnel depth are also investigated. The numerical results show that the porous shock absorption layer has good shock absorption performance and can effectively reduce the maximum internal force of the secondary lining, but it cannot reduce the maximum horizontal relative displacement of the secondary lining. The circumferential rubber strip in the porous shock absorption layer will reduce shock absorption performance. The results of parameter analysis indicate that the shock absorption performance of the porous shock absorption layer increases with the increase of the void ratio and thickness, and it has good shock absorption performance under different surrounding rock grades and tunnel depths.

关键词： tunnel porous shock absorption layer shock absorption performance reaction acceleration method parameter analysis

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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 labor costs. © 2024, The Authors. All rights reserved.

关键词： Robots

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An engineering-oriented multi-fidelity surrogate modeling approach using multisource data aggregation in data-scarce environments

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Journal of Building Engineering 2025年 108卷

作者： Wang, Zhaoyong Cao, Zhengfeng Mulyanto, Joshua Adriel Wu, Yue Zheng, Chaorong School of Civil Engineering Harbin Institute of Technology Harbin150090 China China Construction Machinery Co. LTD Hebei Langfang065000 China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China

The multi-fidelity surrogate (MFS) model has become an emerging hotspot in engineering prediction and optimization. However, this model typically relies on abundant low-fidelity data (LF data) and a limited amount of high-fidelity data (HF data). In practical engineering, data-scarce environments make it difficult to obtain sufficient LF data, leading to a significant decline in MFS model performance. To address this issue, we propose a multi-fidelity surrogate model based on multi-source data aggregation (MDA-MFS), which effectively balances predictive accuracy and modeling cost. Firstly, we utilize a combination of LF data and HF data to construct a data augmentation strategy, generating medium-fidelity data (CF data), which increases the data diversity and fidelity categories without increasing the sample size. Secondly, we build the MDA-MFS model that integrates LF, CF, and HF data, achieving a balance between modeling efficiency and cost through innovative MFS modeling strategies. We validate the practicality of the MDA-MFS model in the engineering application of wind pressure prediction. Finally, through testing on high-dimensional functions (dimensions of 10, 30, and 100), we demonstrate that the MDA-MFS model has good generalizability and scalability. Our findings are encouraging in that the MDA-MFS model demonstrates excellent prediction accuracy and robust generalization performance. Compared with the MFS model, the MDA-MFS model significantly reduces modeling costs while markedly improving modeling efficiency. More importantly, in light of the growing desire for efficient solutions to prediction or optimization problems in data-scarce environments, our results hold broader significance. © 2025 Elsevier Ltd

关键词： Work simplification

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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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Nodes2STRNet for structural dense displacement recognition by deformable mesh model and motion representation

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International Journal of Mechanical System dynamics 2023年第3期3卷 229-250页

作者： Jin Zhao Hui Li Yang Xu Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of TechnologyHarbinChina Key Lab of Structures Dynamics Behavior and Control of the Ministry of Education Harbin Institute of TechnologyHarbinChina School of Civil Engineering Harbin Institute of TechnologyHarbinChina Department of System Design and Simulation Goldwind Science and Technology Co.Ltd.BeijingChina

Displacement is a critical indicator for mechanical systems and civil *** vision-based displacement recognition methods mainly focus on the sparse identification of limited measurement points,and the motion representation of an entire structure is very *** study proposes a novel Nodes2STRNet for structural dense displacement recognition using a handful of structural control nodes based on a deformable structural three-dimensional mesh model,which consists of control node estimation subnetwork(NodesEstimate)and pose parameter recognition subnetwork(Nodes2PoseNet).NodesEstimate calculates the dense optical flow field based on FlowNet 2.0 and generates structural control node ***2PoseNet uses structural control node coordinates as input and regresses structural pose parameters by a multilayer perceptron.A self-supervised learning strategy is designed with a mean square error loss and L2 regularization to train *** effectiveness and accuracy of dense displacement recognition and robustness to light condition variations are validated by seismic shaking table tests of a four-story-building *** studies with image-segmentation-based Structure-PoseNet show that the proposed Nodes2STRNet can achieve higher accuracy and better robustness against light condition *** addition,NodesEstimate does not require retraining when faced with new scenarios,and Nodes2PoseNet has high self-supervised training efficiency with only a few control nodes instead of fully supervised pixel-level segmentation.

关键词： structural dense displacement recognition deformable structural mesh model deep-learning-based monocular vision self-supervised learning

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Optimal placement of multiple types of sensors based on Bayesian system identification using heuristic-enhanced deep reinforcement learning

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Engineering structures 2025年 338卷

作者： Zhong, Qunhong Wei, Shiyin Huang, Yong Jia, Kailiang Li, Hui Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education School of Civil Engineering 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 Harbin China

The advanced structural health monitoring system, integrating multiple types of sensors, provides a comprehensive method for monitoring structural information, effectively identifying abnormal changes, and ensuring the safety of structures in service. However, determining the optimal placement of multiple types of sensors is a challenging combinatorial optimization problem due to its discrete and non-convex nature. The algorithm for optimizing multi-type sensor layouts must be able to handle these complexities effectively. In this paper, a novel multi-type sensor optimal placement algorithm based on heuristic-enhanced deep reinforcement learning (DRL) is proposed. This approach improves algorithm efficiency by initializing with high-quality configurations generated during agent's exploration process. Based on the information entropy and Bayesian theory, we establish a reward function to evaluate the effectiveness of multi-type sensor placement schemes. Three case studies are presented to verify the performance of the proposed algorithm in optimizing sensor placement. Our experiments indicate that adding excessive sensors does not significantly improve monitoring effectiveness, which highlights the necessity of strategically positioning sensors at the most critical data points by optimizing sensor placement to achieve efficient and effective monitoring. We find that incorporating heuristic-based exploration experience significantly boosts the optimization performance of deep reinforcement learning (DRL) algorithm, yielding superior results. The comparison study with other optimization methods also highlights the higher efficiency of our proposed method. © 2025

关键词： Convex optimization

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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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