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SSRN 2021年

作者： Lu, Rongwei Guan, Xinchun Zhang, Chenchen Kou, Yufeng Li, Jinglu Li, Yazhao Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China Harbin Institute of Technology Harbin150090 China

According to the performance requirements of concrete repair materials, a new cement-based permeable crystalline repair material（CCRM） was prepared, and its repair performance and repair mechanism were studied by means of macro mechanical property test, micro ESEM and XRD. The experimental results show that the prepared repair material not only has good flow performance and strength, but also enhances the strength of the repair interface, so that the repair interface is no longer the weak point of the repaired specimen. Through the observation of the microstructure of the repaired specimen, it is found that CCRM can penetrate into the interface of the repaired specimen to form a cross transition zone, and then form a better integration with the repaired specimen, with very good compatibility. Through the test of CCRM repair products by XRD and TG, it is found that there is an active component in CCRM, which can consume calcium hydroxide to form a complex, promote the transformation of calcium carbonate and C-S-H, and then enhance the repair effect. © 2021, The Authors. All rights reserved.

关键词： Repair

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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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Deep-Learning-Based Bridge Condition Assessment by Probability Density Distribution Reconstruction of Girder Vertical Deflection and Cable Tension Using Unsupervised Image Transformation Model 10th

Deep-Learning-Based Bridge Condition Assessment by Probabili...

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10th European Workshop on Structural Health Monitoring, EWSHM 2020

作者： Xu, Yang Tian, Yadi Zhang, Yufeng Li, Hui 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 Dynamics Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China School of Civil Engineering Harbin Institute of Technology Harbin150090 China State Key Laboratory of Safety and Health for Inservice Long Span Bridges Nanjing211112 China

ISBN: (纸本)9783030649074

This study proposes a deep-learning-based condition assessment approach by reconstructing marginal probability density distributions of girder vertical deflection (GVD) and cable tension (CT) using unsupervised image transformation (UNIT) model for a real cable-stayed bridge. 27 and 139 sensors of GVD and CT are distributed along the full bridge, and both the sampling frequencies are 2 Hz. First, vehicle-induced components of GVD and CT are extracted by data pre-processing while the temperature-induced components are removed, a time window of 3 h with a sliding length of 10 min is used to obtain original GVD and CT segments, and the corresponding margin probability density distributions (PDFs) are calculated by kernel density estimation. The training and test sets consist of 2986 and 6236 PDFs, respectively. The proposed UNIT model consists of a series of variational autoencoders (VAEs) and generative adversarial networks (GANs). Both the PDFs of GVD and CT are used as inputs. Then, the proposed UNIT model is updated by solving the mini-max problem of training GANs, in which VAEs act as generative models. Finally, the Wasserstein distance between the predicted and ground-truth PDFs of CT acts as the indicator of bridge condition change. Results show that specific modes of PDF variations induced by SHM system upgrade, cable damage, data anomaly, and traffic jam can be recognized to assess bridge condition. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Discovering Governing Equations by Machine Learning implemented with Invariance

arXiv

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arXiv 2022年

作者： Chen, Chao Jin, Xiaowei Li, Hui 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 Dynamics Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China School of Civil Engineering Harbin Institute of Technology Harbin150090 China

The partial differential equation (PDE) plays a significantly important role in many fields of science and engineering. The conventional case of the derivation of PDE mainly relies on first principles and empirical observation. However, the development of machine learning technology allows us to mine potential control equations from the massive amounts of stored data in a fresh way. Although there has been considerable progress in the data-driven discovery of PDE, the extant literature mostly focuses on the improvements of discovery methods, without substantial breakthroughs in the discovery process itself, including the principles for the construction of candidates and how to incorporate physical priors. In this paper, through rigorous derivation of formulas, novel physically enhanced machining learning discovery methods for control equations: GSNN (Galileo Symbolic Neural Network) and LSNN (Lorentz Symbolic Neural Network) are firstly proposed based on Galileo invariance and Lorentz invariance respectively, setting forth guidelines for building the candidates of discovering equations. The adoption of mandatory embedding of physical constraints is fundamentally different from PINN in the form of the loss function, thus ensuring that the designed Neural Network strictly obeys the physical prior of invariance and enhancing the interpretability of the network. By comparing the results with PDE-NET in numerical experiments of Burgers equation and Sine-Gordon equation, it shows that the method presented in this study has better accuracy, parsimony, and interpretability. © 2022, CC BY.

关键词： Numerical methods

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Optimal overlays for preservation of concrete in cold climate: decision-making by the method of fuzzy comprehensive evaluation combined with AHP

引用

Journal of Infrastructure Preservation and Resilience 2021年第1期2卷 1-16页

作者： Li, Zhipeng Dang, Yudong Tang, Zhen Xie, Ning Lu, Shuang Shi, Xianming Department of Civil & amp Environmental Engineering Washington State University PullmanWA99164-2910 United States Yunnan Key Laboratory of Building Structure and New Materials Yunnan Institute of Building Research Kunming650223 China Key Lab of Structure Dynamic Behavior and Control Ministry of Education Heilongjiang Harbin150090 China School of Civil Engineering Harbin Institute of Technology Heilongjiang Harbin150090 China Shandong Provincial Key Laboratory of Preparation and Measurement of Building Materials University of Jinan Shandong Jinan250022 China

Overlays have been extensively employed as an effective preservation or rehabilitation tool to extend the service life of concrete bridges and pavements, especially concrete slabs suffering from salt scaling and abrasion. However, limited attention has been paid to the durability and performance of these overlays which can be jeopardized when they are exposed to freeze/thaw and wet/dry cycles, deicer applications, studded tires, and their coupled effects. Various overlays feature different engineering properties, and they might be only effective in specific service environments but not in others, and research is lacking to examine their ability to adapt to different environments. This study subjected five overlay products on concrete slabs to the combined action of freeze/thaw (F/T) and wet/dry (W/D) cycles with periodical exposure to either 15 wt.% NaCl solution or 15 wt.% MgCl2 solution, to simulate the typical field scenarios in an accelerated manner. The bond strength, splitting tensile strength, and abrasion resistance of the overlaid concrete slabs were tested to evaluate the effectiveness of various overlays against the deicer scaling and the abrasion by studded tires. Based on the experimental data, this study demonstrated a multi-criteria decision-making method, fuzzy comprehensive evaluation (FCE) combined with analytic hierarchy process (AHP), for the selection of optimal overlays in three different service scenarios (e.g., states of Washington and Oregon [USA] and British Columbia [Canada]). The analysis results indicate that one epoxy overlay exhibited the comprehensively best performance and could be a promising candidate in all three given scenarios, another polymer overlay took second place, while the adaptability of the three cement-based overlays varied in different environments. Graphical Abstract: (Figure presented.). © The Author(s) 2021.

关键词： Sodium chloride

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Interface Shear Analysis of UHPC-NC with U-Shaped Shear Stud

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ce/papers 2023年第5期6卷 1338-1343页

作者： Lu, Ya Lin, Lanri Zhang, Xing Shi, Xinbo Wang, Xiao Wang, Hao Yang, Tao Zhang, Chengwu Wang, Rui Wu, Xiangguo School of Civil Engineering Harbin Institute of Technology Harbin150090 China College of Civil Engineering Fuzhou University Fuzhou China CGN New Energy Holdings Co. Ltd China Heilongjiang Construction Group Co. Ltd China Heilongjiang Wujian Construction Engineering Co. Ltd China Jiangxi Building Materials Research and Design Institute Co. Ltd China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of the Industry and Information Technology Harbin Institute of Technology Harbin150090 China

The composite structure composed of ultra-high performance concrete (UHPC) and normal concrete (NC) has been widely applied to engineering structures. U-shaped shear studs play an important role in the combination of the UHPC-NC interface. To investigate the interface shear characteristics of UHPC-NC with U-shaped shear studs, the numerical model with 14 various parameters is calculated based on the interface push tests. The influences of diameter, stud number, NC elastic modulus, NC compressive strength on the shear characteristics of the UHPC-NC interface are analyzed. The results show that the load-slip curves of the UHPC-NC interface with a U-shaped shear stud have three stages: elastic stage, elastoplastic stage and descending stage. The fitting formula with the power function as the basic function can well express the influence law of diameter on the interface shear capacity. The interface shear capacity is not sensitive to the elastic modulus and compressive strength of NC, and it does not increase or decrease with the increase of stud number, the shear strength of a single stud tends to be constant. © 2023 Ernst & Sohn GmbH.

关键词： Compressive strength

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A change-point detection method for detecting and locating the abrupt changes in distributions of damage-sensitive features of SHM data, with application to structural condition assessment

arXiv

引用

arXiv 2021年

作者： Lei, Xinyi Chen, Zhichng Li, Hui Wei, Shiyin Key Lab of Intelligent Disaster Mitigation The Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China Key Lab of Structures Dynamic Behavior and Control The Ministry of Education Harbin Institute of Technology Harbin150090 China School of Civil Engineering Harbin Institute of Technology Harbin150090 China

Diagnosing the changes of structural behaviors using monitoring data is an important objective of structural health monitoring (SHM). The changes in structural behaviors are usually manifested as the feature changes in monitored structural responses;thus, developing effective methods for automatically detecting such changes is of considerable significance. Existing methods for change detection in SHM are mainly used for scalar or vector data, thus incapable of detecting the changes of the features represented by complex data, e.g., the probability density functions (PDFs). Detecting the abrupt changes occurred in the distributions (represented by PDFs) associated with the damage-sensitive features extracted from SHM data are usually of crucial interest for structural condition assessment;however, the SHM community still lacks effective diagnostic tools for detecting such changes. In this study, a change-point detection method is developed in the functional data-analytic framework for PDF-valued sequence, and it is leveraged to diagnose the distributional information break encountered in structural condition assessment. A major challenge in PDF-valued data modeling or analysis is that the PDFs are special functional data subjecting to nonlinear constraints. To tackle this issue, the PDFs are embedded into the Bayes space, and the associated change-point model is constructed by using the linear structure of the Bayes space;then, a hypothesis testing procedure is presented for distributional change-point detection based on the isomorphic mapping between the Bayes space and a functional linear space. Comprehensive simulation studies are conducted to validate the effectiveness of the proposed method as well as demonstrate its superiority over the competing method. Finally, an application to real SHM data illustrates its practical utility in structural condition assessment. Copyright © 2021, The Authors. All rights reserved.

关键词： Probability density function

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Functional outlier detection for density-valued data with application to robustify distribution to distribution regression

arXiv

引用

arXiv 2021年

作者： Lei, Xinyi Chen, Zhicheng Li, Hui Key Lab of Intelligent Disaster Mitigation 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

Distributional data analysis, concerned with statistical analysis and modeling for data objects consisting of random probability density functions (PDFs) in the framework of functional data analysis (FDA), has received considerable interest in recent years. However, many important aspects remain unexplored, such as outlier detection and robustness. Existing functional outlier detection methods are mainly used for ordinary functional data and usually perform poorly when applied to PDFs. To fill this gap, this study focuses on PDF-valued outlier detection, as well as its application in robust distributional regression. Similar to ordinary functional data, detecting the shape outlier masked by the "curve net’’ formed by the bulk of the PDFs is the major challenge in PDF-outlier detection. To this end, we propose a tree-structured transformation system for feature extraction as well as converting the shape outliers to easily detectable magnitude outliers, relevant outlier detectors are designed for the specific transformed data. A multiple detection strategy is also proposed to account for detection uncertainties and to combine different detectors to form a more reliable detection tool. Moreover, we propose a distributional-regression-based approach for detecting the abnormal associations of PDF-valued two-tuples. As a specific application, the proposed outlier detection methods are applied to robustify a distribution-to-distribution regression method, and we develop a robust estimator for the regression operator by downweighting the detected outliers. The proposed methods are validated and evaluated by extensive simulation studies or real data applications. Relevant comparative studies demonstrate the superiority of the developed outlier detection method with other competitors in distributional outlier detection. © 2021, CC BY.

关键词： Statistics

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Mechanical properties of extrusion-based 3D-printed concrete considering the thickness of the printed layer and printing time interval

引用

structures 2025年 78卷

作者： Tang, Boyang Yan, Jiachuan Han, Xiaoyu Lin, Yini Fan, Feng 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 Engineering Harbin Institute of Technology Harbin150090 China

This paper investigated the performance of 3D printed concrete with a focus on the bonding layer, considering the thickness of the printed layers. Through experimental testing and numerical simulation, the mechanical properties and failure mechanisms of the bonding layer were thoroughly analyzed. The results revealed that compressive strength diminishes with thinner layers and longer printing intervals, while anisotropic mechanical properties become increasingly pronounced with reduced layer thickness. The static performance of 3D printed concrete arches was also studied, showing that increased layer thickness enhances the ultimate load bearing capacity. Cohesive elements were used to simulate compressive strength across varying layer thicknesses and printing time intervals. Prediction formulae for compressive strength were developed and validated through experimental data. The study further simulated the static performance of arches using both cohesive elements and anisotropic material properties, confirming the accuracy of the strength prediction formulae and the effectiveness of the simulation methods. © 2025

关键词： Compressive strength

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Mechanical properties of an innovative variable stiffness bonded-type anchorage for CFRP cables

Transactions of Materials Research

引用

Transactions of Materials Research 2025年第2期1卷

作者： Yanzhao Niu Makeev Aleksander Chen Wang Rui Guo Guijun Xian Key Lab of Structures Dynamic Behavior and Control (Ministry of Education) 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 Harbin Institute of Technology Harbin 150090 China School of Civil Engineering Harbin Institute of Technology Harbin 150090 China

To address the challenges of anchoring Carbon Fiber Reinforced Polymer (CFRP) cables, this paper presents an innovative variable stiffness bonded-type anchorage, which builds upon traditional anchorage designs and incorporates high-temperature resistant bonding media with variable stiffness, along with bent CFRP cables. To evaluate the anchoring performance of this anchorage, mechanical tests were conducted and compared with traditional bond-type anchorage systems. The results show that when the bending angle of CFRP bars exceeds 4°, the tensile strength will be less than 95 % of ultimate strength. As the amount of additive increases, both the tensile strength and elastic modulus of the high-temperature resistant bonding medium gradually improve. The mechanical properties of the high-temperature resistant bonding medium are similar to those of traditional bonding media, but its glass transition temperature ( T g ) is significantly higher than that of traditional bonding media. Additionally, when the bonding layer thickness exceeds 2 mm, it has no effect on the bonding performance. The average bonding stress gradually decreases as the anchorage length increases and the internal cone angle decreases. Furthermore, the Ф4-7 and Ф4-19 CFRP cables anchored by VB anchorage demonstrate ideal brittle failure modes, with anchorage efficiencies exceeding 95 %, and the average slip amounts are 2.78 mm and 5.44 mm, respectively, and the anchorage demonstrates good stability. In contrast, EP anchorage shows less desirable failure modes, with most anchorage efficiencies falling below 95 %. The average slip amounts are 3.22 mm and 7.62 mm, but the results display greater variability.

关键词： CFRP cable Variable stiffness bonding-type anchorage Tensile test High-temperature resin Anchoring performance

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