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Analysis of micro-disturbance drilling pipe pre-reinforcement method for deep excavations above operating subway tunnels

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Frontiers of Structural and Civil Engineering 2025年

作者： Tang, Wenchong Kong, Xiangxun Tang, Liang Ling, Xianzhang School of Civil Engineering Harbin Institute of Technology Harbin150090 China Chongqing Research Institute Harbin Institute of Technology Chongqing401135 China Key Laboratory of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China

This paper uses the three-dimensional numerical simulation method to analyze the first deep foundation pit project directly above the operating subway in a certain area. The monitoring data were compared with the numerical results to verify the accuracy of the numerical model, and then a series of analyses were performed. The soil beneath the tunnel is the most direct object of tunnel deformation caused by the excavation of deep foundation pits above the tunnel. The rebound deformation of the soil beneath the tunnel forces the tunnel to produce an upward deformation cooperatively. Therefore, after comparing and analyzing the prevention criteria of traditional excavation measures, which were not sufficient for this project, a new method of fortification is proposed for the foundation pit above the tunnel, which is called the micro-disturbance drill pipe pre-reinforcement method (PRM) for the soil beneath the tunnel. The comprehensive parameter analysis of the PRM shows that the PRM can effectively reduce the uplift value of the tunnel, and the reinforcement effect is obvious. © Higher education Press 2025.

关键词： Tunneling (excavation)

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A Study Using the Combined Method of Scientometric and Manual Analysis on the Present Research of Plant Fibres Reinforced Concrete

SSRN

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SSRN 2022年

作者： Pan, Yunshi Li, Shuangxin Deng, Ling Shi, Youyu Wang, Jiarui Zhang, Faqi School of Civil Engineering Northeast Forestry University Harbin150060 China School of Civil Engineering Harbin Institute of Technology Harbin150090 China Key Lab of Structures Dynamic Behaviour and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China Anqiu Water Resources Bureau Anqiu262100 China

As one of the most important supporting technologies to economic development and urbanisation, concrete becomes a crucial link to reduce the global carbon emission and confronts with an unprecedented challenge of decarbonation. The reinforcement by plant fibres in concrete is one of promising approaches to decarbonise concrete. This paper combines scientometric and manual analysis to evaluate the research progress on plant fiber reinforced concrete (PFRC), revealing the prevailing research domains and focuses on PFRC and the work performance of PFRC. The main challenges confronted by PFRC are discussed and and future research scope are proposed at the end. © 2022, The Authors. All rights reserved.

关键词： Thermal insulation

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Experimental investigation of shear behavior in precast concrete beams with steel bolted splice connections

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structures 2025年 78卷

作者： Khalil Abualtayef Wei Zhou School of Civil Engineering Harbin Institute of Technology Harbin 150090 China Key Lab of Structures Dynamic Behavior and Control Ministry of Education School of Civil Engineering Harbin Institute of Technology Harbin 150090 China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology School of Civil Engineering Harbin Institute of Technology Harbin 150090 China

Precast concrete structures with bolted connections offer significant advantages, including shorter construction times, improved quality control, and enhanced structural performance. This study investigates the shear behavior of precast concrete beams with steel-bolted splice connections (PCBSBs), which join the discontinuity of two short concrete beam segments, under monotonic loading. Afterwards, the shear performance of PCBSB beams was experimentally and analytically investigated. Six full-scale PCBSB specimens and one control concrete beam were tested to evaluate the effects of key parameters include the shear span ratio and steel plate thickness. The test results show that the failure modes of PCBSB beams are similar to those of reinforcement concrete beams, and the crack propagations can be well controlled by the steel plate thickness. Using a steel bolted connection with a thickness of 3 mm and 5 mm increased the shear crack load by 9 % and 24 %, respectively. After the loading reached 65 % of the peak load, the deflection of steel bolted connection begins to increase more than the shear load deflection till peak load. For beams with long shear span ratio, increase of steel plate thickness led to increase the stiffness, and peak load. Higher a/d ratios increase deflection and shift failure modes from ductile (shear-compression) to brittle (shear-tension). Finally, the experimental shear capacities of the PCBSBs exceeded the predictions of the GB, ACI, EN, and CSA standards for beams with a/d ratios of 1.5 and 2.0. However, discrepancies were observed for beams with a/d of 3.0, where flexural behavior becomes more dominant. Based on these findings, a preliminary design recommendation was proposed. Further studies were recommended to investigate the performance of beams with shorter a/d ratios and varying dimensions to optimize connection efficiency.

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Big-data driven building retrofitting: An integrated support vector machines and fuzzy c-means clustering method

Big-data driven building retrofitting: An integrated support...

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World Sustainable Built Environment - Beyond 2020, WSBE 2020

作者： Lu, Weizhuo Feng, Kailun Department of Civil Environmental and Natural Resources Engineering Luleå University of Technology Sweden Department of Construction Management Harbin Institute of Technology Harbin150009 China 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 Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China

It has become a mainstream to use physical models to quantify expected energy savings from alternative retrofit methods and technologies. However, they are not suitable for predicting energy use of buildings when detailed and specified input parameters are unavailable. The overall purpose of the research is to support the stakeholders in taking decisions on refurbishments options when not all of physical information is available, in order to achieve the Swedish Energy Agency's measurements of near-zero energy buildings. The research will transfer big data from Swedish Energy Performance Certificates for building retrofitting. A Support Vector Machines and Fuzzy C-means clustering (SVM-FCM) integrated machine learning algorithm is used directly to extract the case-specific knowledge from EPC big data regarding building characteristics and energy saving of retrofit measures. It enables to prioritize retrofit measures and compute their expected energy savings for buildings. This proposed data driven method is an attempt of taking advantage of big data for practical building retrofit selection. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： Support vector machines

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Uniaxial Stress-Strain Relationship and Thermal Properties of Concrete with Fine Recycled Aggregate Under Elevated Temperatures

SSRN

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SSRN 2022年

作者： Zhang, Huan Zhao, Hui Liu, Faqi Yang, Hua Pan, Kainan Key Lab of Structures Dynamic Behaviour 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 Department of Civil Engineering Tianjin University Tianjin30072 China College of Civil Engineering Taiyuan University of Technology Taiyuan030024 China School of Engineering Harbin Institute of Technology Harbin150090 China

The models for the stress-strain relationship under elevated temperatures and thermal properties of recycled aggregate concrete (RAC) are essential in the fire resistance design of RAC structures, while no such models are available for RAC made with fine recycled aggregate (FRA). To establish the above models, 33 RAC prisms with three FRA replacement levels (0%, 50%, and 100%) under five temperatures (20 °C, 200 °C, 400 °C, 600 °C, and 800 °C) were tested for the uniaxial compressive stress-strain relationship;Meanwhile, thermal properties (including the specific heat, thermal conductivity, and thermal expansion) of these concrete mixes under a temperature range of 20-800 °C were also tested. A prediction model for the stress-strain relationship under elevated temperatures was proposed by extending the model for RAC under normal temperatures. Based on the test data, models for the thermal properties of RAC with FRA were also established. Results showed that the FRA does not change the shear failure mode of concrete, and its influence on the stress-strain relationship is independent of temperature;the specific heat increases while the thermal conductivity decreases with the FRA replacement ratio;the thermal expansion of concrete under temperature higher than 500 °C decreases with FRA;the proposed models have a good prediction for the stress-strain relationship and thermal properties of RAC. © 2022, The Authors. All rights reserved.

关键词： Stress-strain curves

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Three-Dimensional Reconstruction and Monitoring of Large-Scale structures via Real-Time Multi-vision System 11th

Three-Dimensional Reconstruction and Monitoring of Large-Sca...

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11th International Symposium on Intelligence Computation and Applications, ISICA 2019

作者： Tang, Yunchao Chen, Mingyou Wu, Xiangguo Huang, Kuangyu Wu, Fengyun Zou, Xiangjun He, Yuxin College of Urban and Rural Construction Zhongkai University of Agriculture and Engineering Guangzhou510225 China Key Laboratory of Key Technology on Agricultural Machine and Equipment College of Engineering South China Agricultural University Guangzhou510642 China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education and School of Civil Engineering Harbin Institute of Technology Harbin150001 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

ISBN: (纸本)9789811555763

A four-ocular vision system is proposed for the three-dimensional (3D) reconstruction of large-scale concrete-filled steel tube (CFST) materials under complex testing conditions. These measurements are vitally important for evaluating the seismic performance and 3D deformation of large-scale specimens. A four-ocular vision system is constructed to sample the large-scale CFST, then point cloud acquisition, filtering, and stitching algorithms are applied to obtain 3D point cloud of the specimen surface. Novel point cloud correction algorithms based on geometric features and deep learning are proposed to correct the coordinates of the stitched point cloud. The proposed algorithms center on the stitching error of the multi-view point cloud and the geometric and spatial characteristics of the targets for error compensation, which makes them highly adaptive and efficient. A high-accuracy multi-view 3D model for the purposes of real-time complex surface monitoring can be obtained via this method. Performance indicators of the two algorithms were evaluated on actual tasks. The cross-section diameters at specific heights in the reconstructed models were calculated and compared against laser range finder data to test the performance of the proposed method. A visual tracking test on a CFST under cyclic loading shows that the reconstructed output well reflects the complex 3D surface after point cloud correction and meets the requirements for dynamic monitoring. The proposed method is applicable to complex environments featuring dynamic movement, mechanical vibration, and continuously changing features. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Vibrations (mechanical)

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A new numerical scheme for simulating non-gaussian and non-stationary stochastic processes

arXiv

引用

arXiv 2020年

作者： Zheng, Zhibao Dai, Hongzhe Wang, Yuyin Wang, Wei Key Lab of Structures Dynamic Behavior and Control Harbin Institute of Technology Ministry of Education Harbin150090 China School of Civil Engineering Harbin Institute of Technology Harbin150090 China

This paper presents a new numerical scheme for simulating stochastic processes specified by their marginal distribution functions and covariance functions. Stochastic samples are firstly generated to automatically satisfy target marginal distribution functions. An iterative algorithm is proposed to match the simulated covariance function of stochastic samples to the target covariance function, and only a few times iterations can converge to a required accuracy. Several explicit representations, based on Karhunen-Loève expansion and Polynomial Chaos expansion, are further developed to represent the obtained stochastic samples in series forms. Proposed methods can be applied to non-gaussian and non-stationary stochastic processes, and three examples illustrate their accuracies and efficiencies. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Machine-learning-based methods for output-only structural modal identification

arXiv

引用

arXiv 2020年

作者： Bao, Yuequan Liu, Dawei Tang, Zhiyi Li, Hui Key Lab of Structures Dynamic Behavior Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China Key Lab of Intelligent Disaster Mitigation Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China

In this study, we propose a machine-learning-based approach to identify the modal parameters of the output-only data for structural health monitoring (SHM) that makes full use of the characteristic of independence of modal responses and the principle of machine learning. By taking advantage of the independence feature of each mode, we use the principle of unsupervised learning, making the training process of the deep neural network become the process of modal separation. A self-coding deep neural network is designed to identify the structural modal parameters from the vibration data of structures. The mixture signals, that is, the structural response data, are used as the input of the neural network. Then we use a complex cost function to restrict the training process of the neural network, making the output of the third layer the modal responses we want, and the weights of the last two layers are mode shapes. The deep neural network is essentially a nonlinear objective function optimization problem. A novel loss function is proposed to constrain the independent feature with consideration of uncorrelation and non-Gaussianity to restrict the designed neural network to obtain the structural modal parameters. A numerical example of a simple structure and an example of actual SHM data from a cable-stayed bridge are presented to illustrate the modal parameter identification ability of the proposed approach. The results show the approach's good capability in blindly extracting modal information from system responses. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A sample-based stochastic finite element method for structural reliability analysis

arXiv

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arXiv 2020年

This paper presents a new methodology for structural reliability analysis via stochastic finite element method (SFEM). A novel sample-based SFEM is firstly used to compute structural stochastic responses of all spatial points at the same time, which decouples the stochastic response into a combination of a series of deterministic responses with random variable coefficients, and solves corresponding stochastic finite element equation through an iterative algorithm. Based on the stochastic response obtained by the SFEM, the limit state function described by the stochastic response and the multidimensional integral encountered in reliability analysis can be computed without any difficulties, and failure probabilities of all spatial points are calculated once time. The proposed method can be applied to high-dimensional stochastic problems, and one of the most challenging issues encountered in high-dimensional reliability analysis, known as Curse of Dimensionality, can be circumvented without expensive computational costs. Three practical ex- Copyright © 2020, The Authors. All rights reserved.

关键词： Reliability analysis

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Optimisation route of embodied carbon emissions and cost of prefabricated buildings: A case study of prefabricated shear wall residential structures

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Energy and Buildings 2025年 343卷

作者： Yifan Dong Qingpeng Man Yadi Zhang Yuan Chang School of Civil Engineering Harbin Institute of Technology Harbin 150001 China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin 150009 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 150009 China School of Management Science and Engineering Central University of Finance and Economics Beijing 100081 China

Prefabricated buildings are increasingly replacing traditional cast-in-place concrete structures to reduce construction time and environmental impacts. However, prefabricated components frequently struggle to simultaneously achieve economic cost-effectiveness and carbon emissions reduction. Thus, the construction industry urgently requires component-level technical guidance on prefabricated construction to ensure economic feasibility while also supporting carbon neutrality goals. This study used a typical prefabricated shear wall residential building as a case study to improve the economic and environmental performance of prefabricated building construction. Seven types of structural components are examined, including interior shear walls (ISW) and exterior shear walls (ESW), composite floor slabs (CFS), air-conditioning panels/canopies (A/C), stair landings (SL), beams (BM), and stair flights (SF). By systematically replacing cast-in-place components with their prefabricated counterparts using Revit and Glodon soft tools, the study quantifies and analyses the differences in carbon emissions and costs. The key results are: (i) The optimal replacements for minimising carbon emissions are “BM → SL → SF → CFS → ESW → ISW → A/C,” whereas the most economical cost-effective sequence is “BM → SL → SF → A/C → ISW → ESW → CFS.” (ii) Prefabrication does not universally result in lower carbon emissions. Among the seven types of components, four exhibit higher carbon emissions per unit volume of concrete than their cast-in-place counterparts, with the highest value of CFS (86.2 kg/m 3 ). (iii) To achieve carbon emissions reduction through prefabrication, an optimal prefabrication rate of 3.8–10.6 % is recommended. Within this range, increasing the prefabrication rate results in cost increases while enabling carbon reduction. However, exceeding the 10.6 % cap rate results in both higher costs and carbon emissions compared to traditional cast-in-place methods.

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