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Compressive Strength Prediction of Concrete Under Sulfate Attack Using Coupled Machine Learning Methods

Iranian Journal of Science and Technology - Transactions of Civil Engineering 2024年第2期49卷 1577-1590页

作者： Jin, Libing Liu, Peng Fan, Tai Wu, Tian Wang, Yuhang Wu, Qiang Xue, Pengfei Zhou, Pin Institute of Long-Term Performance On Concrete Structures Henan University of Technology Zhengzhou 450001 China Henan Key Laboratory of Grain and Oil Storage Facility and Safety HAUT Zhengzhou 450000 China

One of the most significant factors affecting the durability of concrete is sulfate attack. In this paper, to predict the compressive strength (CS) of concrete under sulfate attack, three coupled machine learning methods (SSA-BP, PSO-BP and NGO-BP) were developed by coupling BP neural networks (BPNN) with three swarm intelligence algorithms, which are sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO) and northern goshawk optimization algorithm (NGO), respectively. Twelve influencing factors related to material composition, erosion medium and exposure conditions are chosen as inputs, and the CS of concrete subject to sulfate attack is selected as the output. The database of 591 samples collected from published literatures is divided into three parts. Performance indexes are used to evaluate the three coupled models and BP independent model. Finally, the influence of each input on the CS of concrete under sulfate attack is examined using the Grey relational analysis approach. The following findings are reached: (1) all coupled models can predict the CS of concrete under sulfate attack with higher accuracy and achieve better performance than BP independent model, and the best one is SSA-BP model. Benefitted both from the strong nonlinear mapping ability of BPNN and from the global search and fast convergence ability of SSA, SSA-BP model has strong potential in predicting the CS of sulfate attack concrete. (2) Grey relational analysis shows that, among the twelve inputs considered, the initial compressive strength of concrete has the highest correlation (almost one) with the CS of concrete under sulfate attack. The robustness of the suggested model is confirmed by the relational analysis of all input parameters. (3) In addition, this model can provide an innovative way to assess the durability of concrete under complex or harsh environmental conditions. © The Author(s), under exclusive licence to Shiraz University 2024.

关键词： Compressive strength Concrete intelligent optimization algorithm Machine learning Sulfate attack

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An Improved Visualization Modelling Method of Greenhouse Tomato Plants Based on L-system

An Improved Visualization Modelling Method of Greenhouse Tom...

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Chinese Automation Congress (CAC)

作者： Lu, Chenyao Deng, Li Fei, Minrui Shanghai Univ Shanghai Key Lab Power Stn Automat Technol Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

ISBN: (纸本)9781467371896

Visualization technology of plant is important to guide the morphological model construction and the growth state monitoring. Currently, the research mainly focused on the technology of 3D reconstruction based on the real image information and the L-system method based on the morphological structure of the plant. This paper builds the virtual plant by exerting the digital image processing technology to combine the traditional L-system method with the environmental factors of tomato plants. By studying the topology of tomato plants, this paper works on the growth rules deduction of L-system, and researches on the construction of virtual plant model based on the deducted growth rules by introducing the selection and elimination mechanism of intelligent optimization algorithm. At last, the greenhouse tomato plant 3D visual model is finished to meet the requirements of real-time and authenticity.

关键词： L-system virtual visualization digital image processing intelligent optimization algorithm

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Research on the Model of Matching Inventory Slab with Order Contracts of Steel Enterprises 153rd

Research on the Model of Matching Inventory Slab with Order ...

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153rd Annual Meeting & Exhibition of The-Minerals-Metals-and-Materials-Society (TMS)

作者： Li, Cheng-hong Zhu, Ming-mei Zhang, Xian-wu Jiang, Kun-chi Chongqing Univ Coll Mat Sci & Engn Chongqing 400044 Peoples R China

ISBN: (纸本)9783031503511;9783031503498;9783031503481

This paper studies the matching problem between inventory board and customer order. On the basis of objective analysis and constraint analysis of inventory board and order combination process, a nonlinear integer programming model with minimum surplus material ratio as objective function is established, and the model is solved based on particle swarm optimization algorithm. Adaptive weight and adaptive mutation are introduced to improve the algorithm, which improves the global search ability of the algorithm and quickly obtains the global optimal solution. The improved particle swarm optimization algorithm is used to solve the mathematical model, and the results show that the model can describe the practical problems well. Compared with manual experience, the efficiency of the combined process is faster, the residual material rate is lower, the average residual material rate is controlled below 1% and even can be controlled at 0.2% in large-scale application, and the combined process is optimized.

关键词： Slab allocation Combination optimization intelligent optimization algorithm Mathematical model Particle swarm optimization

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A Multi-Strategy Improved Golden Jackal optimization algorithm Integrating the Golden Sine Mechanism 24

A Multi-Strategy Improved Golden Jackal Optimization Algorit...

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3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Li, Zhenyu Hua, Zexi Pang, Yanjie SouthWest Jiaotong Univ Chengdu 610031 Sichuan Peoples R China Sichuan Dory Cancon Technol Co Chengdu 610000 Sichuan Peoples R China

ISBN: (纸本)9798400718212

In response to the shortcomings of poor population diversity, weak global search ability, and susceptibility to local optima in the Golden Jackal optimization algorithm, this paper proposes a Multi-Strategy Improvement GJO (MSIGJO) algorithm that integrates the Golden Sine mechanism. Firstly, Latin hypercube sampling is used to initialize the golden jackal population, improving the quality of initial solutions. Secondly, by incorporating the golden sine mechanism as an operator into the search stage of the Golden Jackal algorithm, the optimization accuracy of the algorithm is improved. Finally, the adaptive t-distribution is used to perturb the optimal individual adaptively, and greedy strategies are employed to find the optimal solution. The comparison test results of MSIGJO and five other intelligent algorithms through 8 benchmark test functions show that the improved algorithm in this paper is superior to different algorithms in terms of convergence speed and optimization.

关键词： intelligent optimization algorithm Golden Jackal optimization algorithm Latin hypercube sampling Golden Sine mechanism adaptive t-distribution

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An Always Best Connected-Supported intelligent Handover Decision Mechanism Based on Improved Artificial Fish Swarm algorithm 17

An Always Best Connected-Supported Intelligent Handover Deci...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Jimei He Xingwei Wang Min Huang College of Software College of Computer Science & Engineering College of Information Science & EngineeringNortheastern University

ISBN: (纸本)9781509039456;9781509039449

In order to cope with the rapid development of the Internet and to solve the lack of IP address resources, this paper proposes an ABC(Always Best Connected)-supported intelligent handover decision mechanism based on Improved Artificial Fish Swarm algorithm(IAFSA). The proposed mechanism takes into consideration both the user side and the network side to support ABC better. We first utilize the triangular fuzzy number analytic hierarchy process to calculate the weight of the QoS parameter, which can ensure the rationality of the weight setting. Then, we introduce the game theory and the Nash equilibrium to consider the benefits of both network providers and end-users to ensure the fairness of handover decision. Finally, based on the NS2(Network Simulator 2) simulation platform, the IAFSA based intelligent handover decision mechanism is evaluated and compared with the existing handover mechanism in terms of utility, satisfaction, and suitability performance. Simulation results show that our mechanism is feasible and effective.

关键词： IAFSA ABC intelligent optimization algorithm handover decision

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Fault Diagnosis of Modular Multilevel Converter Based on Optimized Support Vector Machine 39

Fault Diagnosis of Modular Multilevel Converter Based on Opt...

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39th Chinese Control Conference (CCC)

作者： Ke, Longzhang Liu, Zhenxing Zhang, Yong Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Huanggang Normal Univ Sch Electromech & Automobile Engn Huanggang 438000 Peoples R China

ISBN: (数字)9789881563903

ISBN: (纸本)9789881563903

In this paper, the open-circuit fault characteristics of IGBT in Modular multilevel converter(MMC) are studied. A fault diagnosis method based on average value of the three-phase ac current to extract fault characteristics, then an intelligent optimization algorithm for fault detection and location of support vector machine(SVM) is proposed. When a fault occurs, the average value of three-phase ac current is no longer zero, which can detect the occurrence of the faulty SM. When the open-circuit fault occurs in the SMs of different bridge aims, the average value of three-phase ac current produces different de bias components. According to the change rule of average value in three-phase current, the fault characteristic is extracted, and then the fault is classified and located by the SVM classifier optimized by genetic algorithm. Finally, Simulation and experimental results show that the proposed method can not only detect the fault block module quickly, but also improve the accuracy of fault classification.

关键词： Modular multilevel converter(MMC) fault diagnosis support vector machine(SVM) intelligent optimization algorithm

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A Novel Node Localization algorithm Based on Sparrow Search for WSNs 11

A Novel Node Localization Algorithm Based on Sparrow Search ...

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11th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Zhang, Quanfu Zhang, Yaming Zhu, Xiaoli Yunnan Normal Univ Minist Educ Key Lab Educ Informatizat Nationalities Kunming Yunnan Peoples R China

ISBN: (纸本)9780738111353

In the application of wireless sensor networks, It is very important to determine the position of nodes themselves. In recent years, the research of node localization in wireless sensor networks based on intelligent optimization algorithm has attracted much attention. Sparrow search algorithm is a new intelligent optimization algorithm. In this paper, it is applied to the localization of wireless sensor network for the first time, and proposed a novel WSNs localization algorithm. The simulation results show that, compared with similar algorithms, the algorithm proposed in this paper not only achieves higher localization accuracy, but also shows better anti ranging error performance, and requires fewer anchor nodes for the same localization accuracy.

关键词： wireless sensor networks node localization intelligent optimization algorithm sparrow search

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Optimal Search Mechanism Analysis of Light Ray optimization algorithm

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Journal of Mathematical Research with Applications 2012年第5期32卷 530-542页

作者： Jihong SHEN1,Jialian LI2,,Bin WEI1 1.College of Science,Harbin Engineering University,Heilongjiang 150001,P.R.China 2.College of Automation,Harbin Engineering University,Heilongjiang 150001,P.R.China College of Science Harbin Engineering University Heilongjiang 150001 College of Automation Harbin Engineering University Heilongjiang 150001

Based on Fermat's principle and the automatic optimization mechanism in the propagation process of light,an optimal searching algorithm named light ray optimization is presented,where the laws of refraction and reflection of light rays are integrated into searching process of *** this algorithm,coordinate space is assumed to be the space that is full of media with different refractivities,then the space is divided by grids,and finally the searching path is assumed to be the propagation path of light *** the law of refraction,the search direction is deflected to the direction that makes the value of objective function *** the law of reflection,the search direction is changed,which makes the search continue when it cannot keep going with *** the function values of objective problems are used and there is no artificial rule in light ray optimization,so it is simple and easy to *** analysis and the results of numerical experiments show that the algorithm is feasible and effective.

关键词： Fermat’s principle intelligent optimization algorithm light ray optimization optimal search mechanism.

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Research and Analysis of Obstacle Avoidance Path Planning for Autonomous Vehicles

Research and Analysis of Obstacle Avoidance Path Planning fo...

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2021 International Conference on Signal Processing and Machine Learning, CONF-SPML 2021

作者： Qinyang,Lv Jincheng No.2 Middle School

ISBN: (纸本)9781665417341

Obstacle avoidance path planning is a key technology for autonomous vehicles in identifying obstacles and avoiding obstacles,which is of great significance to the development of autonomous driving *** article gives an overview of traditional algorithms and intelligent algorithms related to obstacle avoidance path planning technology for autonomous vehicles,analyzes,compares and summarizes the advantages and disadvantages of each algorithm,and introduces their combined *** considering the advantages and disadvantages of using a single algorithm to plan obstacle avoidance paths in practical applications,it is found that a single algorithm shows drawbacks in a dynamic environment,such as poor computing ***,it is concluded that the use of multiple algorithms can make up for the shortcomings of a single algorithm,which has many advantages and will be focus of automatic obstacle avoidance research in the future.

关键词： autonomous vehicle obstacle avoidance path planning algorithm traditional obstacle avoidance algorithm intelligent optimization algorithm

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Big Data Feature Mining Method for Energy Storage System of Photovoltaic Power Station in Typical Scenarios 4

Big Data Feature Mining Method for Energy Storage System of ...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Sheng, Xinxin Yang, Qingbo Ye, Ruili Xie, Lin Feng, Chaohan Tao, Lei Zhang, Zhoujie China Electric Power Research Institute Beijing Key Lab of Research and System Evaluation of Power Dispatching Automation Technology Beijing China

ISBN: (纸本)9798350363586

The energy storage system can effectively solve the challenges brought by the high proportion of renewable energy access to the power grid. In this paper, a big data feature mining method based on double-layer intelligent optimization algorithm is proposed. Considering the differences in operating conditions of energy storage systems in photovoltaic power stations caused by different seasonal scenarios and different weather scenarios, the improved bird swarm algorithm is proposed to mine the big data features of energy storage systems in photovoltaic power stations under typical weather scenarios. The inherent operation law of the energy storage system in the actual photovoltaic power station is revealed, and the charging and discharging characteristics of the actual energy storage system are reflected. © 2024 IEEE.

关键词： big data energy storage system feature mining intelligent optimization algorithm photovoltaic power station the dispatching and control cloud

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