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第九届水泥混凝土国际会议

作者： Huanghuang HUANG Xiaojian Gao Guangshuai Han School of Civil Engineering Harbin Institute of Technology Key Lab of Structure Dynamic Behavior and Control(Harbin Institute of Technology).Ministry of Education

This paper aims to investigate the effects of rice husk ash(RHA) on strength and penneability of ultra-high performance concrete(UHPC).The RHA was manufactured by calcining rice husk at temperature of 500 "C and incorporated in UHPC mixture to replace different ratio of SF by *** every mixture,fluidity,compressive and flexural strength,permeability before and after loading and pore structure were measured at different curing *** results show that the addition of RHA can enhance the compressive strength of UHPC at 3,28 and 120 days and refine the pore structure of UHPC measured by MIP method at 3 and 28 *** can also improve the permeability of UHPC before exposure to *** vertical loading or lateral loading,samples incorporated RHA behave better anti-permeability than the control mixture when the loading does not exceed 70%of load ***,RHA may bring about an increase in permeability of UHPC when being subjected to very high levels of loading(above80%).

关键词： Ultra-high performance concrete Rice husk ash Strength development Permeability Loading

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Study on the performance of reinforced concrete blocks treated by styrene-acrylic emulsion

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Chemical Engineering Transactions 2018年 66卷 85-90页

作者： Zhu, Jing Zheng, Wenzhong Xie, Lili Wu, Yunqiang Zhang, Yongxin Fu, Junxiang Key Laboratory of Earthquake Engineering and Engineering Vibration Institute of Engineering Mechanics China Earthquake Administration Harbin150080 China College of Civil Engineering and Architecture Harbin University of Science and Technology Harbin150080 China Key Lab of Structures Dynamic Behavior and Control of the Ministry Education Harbin Institute of Technology Harbin150090 China

This paper introduces a method of improving the performance of reinforced concrete blocks and reveals the reinforcing effect of fibers. It is pointed out that the plant fibers treated by styrene-acrylic emulsion can effectively improve the interfacial bonding conditions, for the modified straw fibers which were treated by alkali processing and acrylic coating, their performance has been significantly improved, which is conducive to absorbing energy and improving the strength of the material. This paper focuses on introducing three types of reinforced concrete blocks, it’s known that the bridging effect can effectively prevent the development of micro cracks. The occurrence of cracks increases the macroscopic tensile strain of the material and increases the ductility and toughness of the matrix. Studying plant fiber reinforced blocks will help promote the application of this new wall materials. Copyright © 2018, AIDIC Servizi S.r.l.

关键词： Tensile strain

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Wind load prediction of large-span dry coal sheds based on grnn and its application 9th

Wind load prediction of large-span dry coal sheds based on g...

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9th International Structural Engineering and Construction Conference: Resilient structures and Sustainable Construction, ISEC 2017

作者： Sun, Ying Yang, Lin Wu, Yue Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education School of Civil Engineering Harbin Institute of Technology Harbin China School of Civil Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9780996043748

The distribution and fluctuation of wind load on large-span dry coal sheds are complicated. Wind load on typical shape of roofs can be sometimes determined based on the wind tunnel tests carried out on roofs of similar shape. To expand the application scope of the test data, Generalized Regression Neural Network (GRNN) is introduced. The prediction models on large-span dry coal are given, where the wind load is expressed by eight parameters: mean, RMS, skewness, kurtosis of wind pressure coefficients, three auto-spectral parameters (including descendent slope in high frequency range, peak reduced spectrum and reduced peak frequency) and coherence exponent for cross-spectra. Cross validation and trails are carried out to determine the parameter in the GRNN model. Further, the wind load prediction is applied on a dry coal shed shell. The wind-induced responses are calculated and compared with the results of wind tunnel tests, with extremely close result. Therefore, it can be concluded that GRNN is feasible in predicting wind load on roof structures. Copyright © 2017 ISEC Press.

关键词： Neural networks

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Chemical composition of calcium-silicate-hydrate gels: Competition between kinetics and thermodynamics

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Physical Review Materials 2019年第6期3卷 065603-065603页

作者： Tao Du Hui Li Qi Zhou Zhe Wang Gaurav Sant Joseph V. Ryan Mathieu Bauchy Key Lab of Structures Dynamic Behavior and Control (Harbin Institute of Technology) Ministry of Education 150090 Harbin China School of Civil Engineering Harbin Institute of Technology 150090 Harbin China Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab) Department of Civil and Environmental Engineering University of California Los Angeles California 90095 USA Laboratory for the Chemistry of Construction Materials (LC2) Department of Civil and Environmental Engineering University of California Los Angeles California 90095 USA California Nanosystems Institute (CNSI) University of California Los Angeles California 90095 USA Energy and Environment Directorate Pacific Northwest National Laboratory Richland Washington 99352 USA

Although concrete is the world's most widely used manufactured material, basic questions persist regarding the nature of its binding phase, namely, the calcium-silicate-hydrate (C-S-H) gel. Here, based on reactive molecular dynamics simulations, we address the following fundamental question: Is the chemical composition of C-S-H controlled by the kinetics or thermodynamics of its precipitation? As a major outcome of this study, we demonstrate that the average stoichiometry of the C-S-H gel forming upon the hydration of ordinary portland cement (i.e., Ca/Si = 1.75) is determined by the composition of the C-S-H phase presenting the fastest precipitation kinetics, rather than the highest thermodynamic stability.

关键词： Amorphous materials Gels Molecular dynamics

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General solutions for nonlinear differential equations: A deep reinforcement learning approach

arXiv

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arXiv 2018年

作者： Wei, Shiyin Jin, Xiaowei Li, Hui Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China Key Lab of Structures Dynamic Behavior and Control Ministry of Education Harbin Institute of Technology Harbin150090 China School of Civil Engineering Harbin Institute of Technology Harbin150090 China

Physicists use differential equations to describe the physical dynamical world, and the solutions of these equations constitute our understanding of the world. During the hundreds of years, scientists developed several ways to solve these equations, i.e., the analytical solutions and the numerical solutions. However, for some complex equations, there may be no analytical solutions, and the numerical solutions may encounter the curse of the extreme computational cost if the accuracy is the first consideration. Solving equations is a high-level human intelligence work and a crucial step towards general artificial intelligence (AI), where deep reinforcement learning (DRL) may contribute. This work makes the first attempt of applying (DRL) to solve nonlinear differential equations both in discretized and continuous format with the governing equations (physical laws) embedded in the DRL network, including ordinary differential equations (ODEs) and partial differential equations (PDEs). The DRL network consists of an actor that outputs solution approximations policy and a critic that outputs the critic of the actor's output solution. Deterministic policy network is employed as the actor, and governing equations are embedded in the critic. The effectiveness of the DRL solver in Schrödinger equation, Navier-Stocks, Van der Pol equation, Burgers' equation and the equation of motion are discussed. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Multivariate stationary non-Gaussian process simulation for wind pressure fields

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Earthquake Engineering and Engineering Vibration 2016年第4期15卷 729-742页

作者： Sun Ying Su Ning Wu Yue Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology School of Civil Engineering Harbin Institute of Technology

Stochastic simulation is an important means of acquiring fluctuating wind pressures for wind induced response analyses in structural engineering. The wind pressure acting on a large-span space structure can be characterized as a stationary non-Gaussian field. This paper reviews several simulation algorithms related to the Spectral Representation Method （SRM） and the Static Transformation Method （STM）. Polynomial and Exponential transformation functions （PSTM and ESTM） are discussed. Deficiencies in current algorithms, with respect to accuracy, stability and efficiency, are analyzed, and the algorithms are improved for better practical application. In order to verify the improved algorithm, wind pressure fields on a large-span roof are simulated and compared with wind tunnel data. The simulation results fit well with the wind tunnel data, and the algorithm accuracy, stability and efficiency are shown to be better than those of current algorithms.

关键词： stochastic simulation non-Gaussian process static transformation method wind pressure field

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Simplified design method and seismic performance of space trusses with consideration of the influence of the stiffness of their lower supporting columns

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Earthquake Engineering and Engineering Vibration 2016年第2期15卷 401-409页

作者： Fan Feng Sun Menghan Zhi Xudong Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology School of Civil Engineering Harbin Institute of Technology

Static and dynamic force performance of two types of space truss structures i.e. square pyramid space truss（SPST） and diagonal on square pyramid space truss（DSPST）, are studied to determine the effect of stiffness of their lower supporting members. A simplified model for the supporting columns and the equivalent spring mass system are presented. Furthermore, the feasibility of the simplified model is demonstrated through theoretical analysis and examples of comparative analysis of the simplified model with the entire model. Meanwhile, from the elastic analysis under frequently occurring earthquakes and elasto-plastic analysis under seldom occurring earthquakes subjected to TAFT and EL-Centro seismic oscillation it is shown that the simplified method can be encompassed in the results from the normal model. It also showed good agreement between the two methods, as well as greatly improved the computational efficiency. This study verified that the dynamic effect of the supporting structures was under considered in space truss design in the past. The method proposed in the paper has important significance for other space truss structures.

关键词： simplified supporting pyramid stiffness truss verified diagonal elasto columns seldom

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Investigation on mapping relationship between deflection and cable tension by artificial neural networks 11

Investigation on mapping relationship between deflection and...

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11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017

作者： Tian, Yadi Li, Hui Key Laboratory of Intelligent Disaster Prevention and Mitigation for Civil Infrastructure Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters Ministry of Industry and Information Technology Key Laboratory of Structural Dynamic Behavior and Control School of Civil Engineering Harbin Institute of Technology Harbin150090 China

ISBN: (纸本)9781605953304

Cables usually serve as critical load-bearing structural components in cable-stayed bridges. Vertical deflection is an important overall indicator that directly reflects the health status of bridge. The relationship between cable tension and vertical deflection also reflects the underlying mechanism of structures. In this context, a machine learning-based approach is proposed. Cable tension and vertical deflection are caused by prestress, temperature and vehicle loads. In this study, we focus on the cable force and vertical deflection caused by vehicle loads. Artificial neural network (ANN) is employed as the supervised learning method to learn the relationship between cable tension and vertical deflection. Field monitored data of Jintang Bridge are used to test the validity of the proposed method. The results show that ANN is capable of modelling vertical deflection from cable tension data in the case of arbitrary vehicle distribution. The proposed method which integrates the local and global variables by using two different kinds of monitoring data, avoids the complicated mechanical derivations, and is proved to be environment-insensitive.

关键词： Neural networks

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Studyon failure modeof husk mortar energy-saving wallboards 9th

Studyon failure modeof husk mortar energy-saving wallboards

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9th International Structural Engineering and Construction Conference: Resilient structures and Sustainable Construction, ISEC 2017

作者： Zhang, Yu Zhang, Qingwen Zhao, Jian Zhou, Guangchun Key Lab of Structures Dynamic Behavior and Control of Ministry of Education Harbin China Key Lab of Smart Prev. and Mitigation of Civ. Eng Disasters of the Min. of Indust. and Info. Technol Harbin China School of Civil Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9780996043748

This paper focuses on husk mortar wallboard, which is a new type of energy-saving composite wallboard with new materials and complex working mechanism. There are eight total different dimensioned panels tested. Six of them are openings (window or door), with different opening rates;the other two are full panels with same dimensions. Based on the experimental data, they are analyzed under both horizontal and vertical direction loading, combined with the finite element analysis to reveal the working characteristics. The finite element model of husk mortar energy-saving wallboards is established by ANSYS software. Finally, the finite element results are compared with the experimental results from three aspects: ultimate load, failure mode and load displacement curve, which verifies the correctness of the finite element model. Copyright © 2017 ISEC Press.

关键词： Mortar

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Data science and engineering for structural health monitoring 8

Data science and engineering for structural health monitorin...

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8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2017

作者： Li, H. Bao, Y.Q. Li, S.L. Ou, J.P. Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China Key Lab of Intelligent Disaster Mitigation of the Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China School of Civil Engineering Harbin Institute of Technology Harbin150090 China

ISBN: (纸本)9781925553055

Structural health monitoring (SHM) technology study the damage or even characteristics of disaster evolving of structures by various types of sensors installed on structures and provide scientific support for the structural design, construction, maintenance and operation safety. The significant scientific issues of SHM can be concluded as 'sensing' and 'data'. According to the widely application of SHM on civil infrastructures and accumulation of huge quantities of data, it is well-recognized that data sciences and engineering for SHM is increasingly becoming a world-wide research focus. The issue of 'data' is core target of SHM which includes the data acquisition, transmission and management, data analysis and mining, structural damage identification and structural safety assessment and so on. This paper reviews the recent research development of data sciences and engineering on our research group which includes data acquisition by compressive sensing based on group sparse optimization, computer vison and deep learning methods for data anomaly detection and structure condition assessment, the identification and modeling of spatio-temporal distribution of vehicle loads, and corrosion damage and crack identification by computer vision ***, the artificial intelligence in civil engineering is the extension of SHM technique, and will make the civil structure have ability of "see", "sensing", "thinking","talking", "action and "adapation" (2S+2T+2A). © 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved.

关键词： Structural health monitoring

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