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Methodology for mapping bridge temperature-deflection considering uncertainty

ENGINEERING STRUCTURES 2025年 326卷

作者： Xiao, Bowen Zheng, Yuanlin Shi, Jiapeng Di, Jin Fan, Jiuhong Qin, Fengjiang Chongqing Univ Sch Civil Engn Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China Chongqing Urban Investment Infrastruct Construct Chongqing 400015 Peoples R China China Harbour Engn Co Ltd Beijing 100027 Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China

Bridge deflection is a critical measure of structural stiffness, influenced by a variety of factors, making it challenging to establish a clear temperature-deflection relationship. Current research separates the temperature- induced deflection component from deflection data. However, the authenticity of this separation has not been adequately verified. This study introduces a novel neural network, the Residual Connection Mixture Density Network (ResMDN), which eliminates the need for preprocessing deflection data. The ResMDN allows for the construction of a probability distribution prediction model for temperature-induced deflection, effectively addressing uncertainties with limited prior knowledge. The research findings demonstrate a significant linear relationship between the ResMDN-predicted variance of temperature-induced deflection and vehicular weight, thereby validating the accuracy of the ResMDN model. Furthermore, the study proposes using the maximum probability density of temperature-induced deflection as the theoretical value. A comparative analysis of deflection components extracted using different methods revealed that traditional approaches tend to capture the combined effect of temperature- and vehicle-induced static deflections. In contrast, the ResMDN accurately predicts the actual temperature-induced deflections, outperforming existing methodologies.

关键词： Structural health monitoring intelligent algorithm Mixture density network Bridge structure Temperature-deflection mapping

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A High-Efficiency and Effectiveness Designing Methodology for Discrete-Coded Energy Selective Surface Based on Machine Learning

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2025年第4期73卷 2427-2437页

作者： Yao, Lixiang Huang, Xianjun Chen, Hongting Jiang, Huan Liang, Yuanlong Liu, Peiguo Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

This article introduces an innovative methodology for the design of energy selective surfaces (ESSs) using machine learning (ML). The methodology differs from traditional ESS design approaches that rely on equivalent circuit models (ECMs). Instead, it involves the training of a forward prediction neural network for the electromagnetic performance of ESS and utilizing an optimization algorithm to achieve the optimal ESS structure. The optimization objective is to align the transmission pole in the transmitted state and transmission zero in the protection state with the target frequency point, achieving a minimal insertion loss (IL) and a maximal shielding efficiency (SE) at the same time. The designed ESS structure consists of a metal frame, a nonlinear induction region, and a resonant region formed by a randomly distributed 0/1 matrix, whose electromagnetic performance varies with the distribution of the 0/1 matrix. Through multiple simulations, 6000 sets of training data are obtained and used to train a fully connected (FC) neural network. Subsequently, the particle swarm optimization (PSO) algorithm is employed to optimize the objective function. Following these procedures, several ESS structures are successfully obtained. After prototype fabrication and testing, all samples demonstrate consistency with expected results by achieving a low IL and a high SE near the target frequencies.

关键词： Electromagnetics Resonance RLC circuits Impedance Protection Neural networks Machine learning Integrated circuit modeling Training Metals Energy selective surface (ESS) forward prediction intelligent algorithm machine learning (ML) neural network

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Efficient energy optimization of large-scale natural gas pipeline network: An advanced decomposition optimization strategy

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CHEMICAL ENGINEERING SCIENCE 2025年 309卷

作者： Peng, Yong Qiu, Rui Zhao, Wei Zhang, Fangliang Liao, Qi Liang, Yongtu Fu, Guangtao Yang, Yi Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China China Univ Petr Natl Engn Lab Pipeline Safety Beijing Key Lab Urban oil & Gas Distribut Technol Fuxue Rd 18 Beijing 102249 Peoples R China PipeChina Engn Technol Innovat Co Ltd Tianjin 300450 Peoples R China PipeChina Oil & Gas Control Ctr Beijing 100013 Peoples R China

The large-scale long-distance natural gas pipeline network is characterized by its substantial transmission capacity and high energy consumption. With the proposal of dual-carbon targets, the accurate evaluation and optimization of energy consumption have become more important. As a nonlinear mathematical problem that maps the real world, it is difficult to solve. Linear approximation and intelligent algorithms are commonly used to address the nonlinear characteristics of this problem. However, achieving satisfactory timeliness and accuracy remains challenging for large-scale pipeline network systems. This paper proposes a decomposition optimization method for natural gas pipeline network based on the Alternating Direction Method of Multipliers. The method decomposes large-scale pipeline systems into different subnetworks and employs intelligent algorithms for solution. It preserves the nonlinear characteristics of natural gas networks, reduces reliance on the performance of intelligent algorithms, and enhances the solution efficiency for complex models. A large-scale parallel pipeline network in western China is taken as a case to verify the effectiveness of the proposed method. The results demonstrate that, compared to traditional methods, the proposed method can reduce solving time 27.12% 93.88%, while further decreasing energy consumption. Also, the maximum energy consumption of the entire system can be reduced by 16.31%. This study can provide theoretical guidance for the operation optimization of large-scale natural gas pipeline networks.

关键词： Natural gas pipeline Energy consumption optimization intelligent algorithm Alternating Direction Method of Multipliers

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Interior sound quality evaluation of high-speed trains-a literature review

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INTERNATIONAL JOURNAL OF RAIL TRANSPORTATION 2025年第2期13卷 287-312页

作者： Qian, Kun Shen, Zhenghua Tan, Jing Liu, Ke Wang, Yanfu Li, Haoyang Zhao, Jian Dalian Univ Technol Sch Mech Engn Room 321BHoi Han Bldg Dalian 116024 Peoples R China

The rapid advancement of the high-speed rail industry has led to an increase in passenger traffic on high-speed trains. As a result, there is an increasing concern about noise issues within the carriages. The evaluation of sound quality in high-speed trains has garnered significant research attention in recent years. The interior sound quality of high-speed trains can be evaluated through human subjective tests or AI-based models, with the latter gaining preference due to their efficiency, reduced labour intensity, and enhanced repeatability. Using AI-based models to objectively evaluate the interior sound quality in high-speed trains, circumvents the need for costly and labour-intensive subjective tests. This paper focuses on the subjective and objective evaluation methods of sound quality in high-speed trains, and the characteristics of sound quality in high-speed trains. Furthermore, this paper discusses the existing quantitative model for evaluating the sound quality of high-speed trains. This paper delves into the merits and demerits of various evaluation methods and models, and endeavours to explore future developments in evaluating interior sound quality in high-speed trains. This paper endeavours to comprehensively analyse current evaluation techniques and models while identifying the gaps and limitations in existing methodologies. Additionally, this paper underscores the necessity for the development of innovative approaches to evaluate sound quality in high-speed trains and proposes potential directions for future research. Overall, this study aspires to enrich the ongoing discourse on sound quality evaluation in high-speed trains and to provide insights that may guide the creation of more effective and reliable evaluation methods.

关键词： High-speed trains noise sound quality evaluation model neural network intelligent algorithm

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Application of F-HGAPSO algorithm in Reservoir Flood Control Optimal Operation

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WATER RESOURCES MANAGEMENT 2025年第4期39卷 1763-1782页

作者： Cui, Guangyun Qi, Zhen Zhao, Huaqing Zhao, Ranhang Wang, Haofang Zhao, Jiaxing Shandong Univ Sch Civil Engn 17923 Jingshi Rd Jinan 250061 Shandong Peoples R China

Flood control operation of reservoir is a high-dimensional and nonlinear problem with numerous constraints, and intelligent algorithms are widely used to solve the flood control optimization problem. The traditional intelligent algorithms often face issues such as slow convergence and a tendency to find local optima when solving flood control optimization problems. This paper proposes a method that couples a hybrid genetic algorithm-particle swarm optimization with fuzzy adaptive inertia weights (F-HGAPSO) to solve the model established for reservoir flood control operation. The initial population is generated from the reservoir discharge taking as the decision variable, initially optimized using the genetic algorithm, and then further optimized to obtain the results using the particle swarm optimization (PSO) with a Mamdani fuzzy system for adaptive inertia weight. Finally, as a case study, the proposed method is applied to solve the model established for Mushan reservoir flood control operation, with a penalty function handling the constraints. To demonstrate the validity of the method, the results obtained by F-HGAPSO are compared and analyzed with those obtained by PSO and the elite genetic algorithm (EGA). The results show that F-HGAPSO outperforms PSO and EGA in solving the flood control model, achieving peak flow reductions of 20.28%-30.27% with rapid convergence in 13-22 iterations, effectively avoiding local optima issues, and that highlights the F-HGAPSO has strong optimization ability and efficiency. The proposed method can be extended to the flood control operation of other reservoirs with similar conditions.

关键词： Reservoir Flood control operation Optimal scheduling intelligent algorithm Peak flow reduction

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Enhanced multivariate data fusion and optimized algorithm for comprehensive quality profiling and origin traceability of Chinese jujube

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FOOD CHEMISTRY-X 2025年 25卷 102190页

作者： Chen, Peng Wang, Xiaoli Fu, Rao Xiao, Xiaoyan Li, Yu Lu, Tulin Wang, Tao Guo, Qiaosheng Zhou, Peina Fei, Chenghao Nanjing Agr Univ Inst Chinese Med Mat Nanjing 210095 Peoples R China Nanjing Univ Chinese Med Coll Pharm Nanjing 210023 Peoples R China Nanjing Res Inst Comprehens Utilizat Wild Plants Inst Plant Resources & Chem Nanjing 210042 Peoples R China Suzhou Liliangji Hlth Ind Co Ltd Suzhou 215000 Peoples R China Nanjing Univ Chinese Med Changzhou Affiliated Hosp Changzhou 213003 Peoples R China

Chinese jujube (CJ) is a nutritious food. Its authenticity has received increasing attention. This research utilized computer vision, ultrafast gas-phase electronic nose, and GC-MS technologies to collect jujube samples from various regions in China, including Xinjiang, Gansu, Shaanxi, Henan, Shandong, and Hebei. Multidimensional trait data, encompassing spectra, texture, and odour, were gathered. By employing multivariate statistical methods, 46 trait characteristic factors (VIP > 1, P < 0.05) were identified and utilized to rapidly differentiate jujube samples originating from different regions. The multivariate statistical analysis and support vector machine (SVM) classification were also combined to develop a novel artificial intelligence algorithm. The accuracy of this innovative method was significantly higher than that of conventional discriminant analysis methods, achieving a perfect 100.0 % accuracy. As a consequence of this research, more intelligent algorithms can be developed that trace the origin of food based on multidimensional data.

关键词： Chinese jujube UF-GC-E-nose GC-MS Multivariate statistics intelligent algorithm Traceability

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intelligent algorithmic Multi-Objective Optimization for Renewable Energy System Generation and Integration Problems: A Review

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2019年第1期9卷 271-280页

作者： Acharya, P. S. Wagh, M. M. Kulkarni, V. V. Shivaji Univ Dept Technol Energy Technol Div Kolhapur 416004 Maharashtra India Sanjay Ghodawat Grp Inst Dept Mech Engn Atigre 416118 Maharashtra India

Integrated renewable energy is now becoming an option for sustainable growth of humanity. Because it provides the uninterrupted energy supply to small, and micro grids, as well as it penetrates the larger conventional energy grid to decrease the emissions and active losses. Combining renewable sources to conventional grid results in an integrated renewable energy system. Sometimes for smaller loads hybrid renewable energy systems (HRES) can be used as an alternative. This whole integration of different renewable energy systems (RES) which can be grid connected or off grid, requires optimization of various factors like total levelized cost of energy, total CO2 emission (life cycle), total percentage of grid penetration etc. Hence, these kinds of problems include a large data regarding, energy resources, their annual availability, use pattern of the energy. Therefore to solve these complex problems one has to use intelligent computer algorithms, because of less calculation time and better accuracy than any other means. This paper highlights an updated literature regarding algorithmic multiobjective optimization for generation and integration side problems of renewable energy resources to satisfy electrical and in some cases thermal demand also. It will be helpful to the researchers working in the field of multiobjective optimization and integrated RES.

关键词： Grid integration Levelized cost Objective function Constraint condition intelligent algorithm

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intelligent algorithms and Their Application in Electrical Impedance Tomography 9th

Intelligent Algorithms and Their Application in Electrical I...

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9th International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Lu, Xin Zhou, Mingyong Beihang Univ Beihai Coll Beihai City 536000 Guangxi Peoples R China

ISBN: (纸本)9783319232041;9783319232034

EIT (Electrical impedance tomography) problem is an inverse problem of mathematical physics. In the past years, numerous scientists and researchers put their effort to this problem and a lot of traditional mathematics algorithms have been applied. The difficulty of this problem is that we do not know inside information, all we know is potential on the surface of the object, then use this known data to calculate inside structure. With the time going by, intelligent algorithms have become more popular. In this paper, we used an intelligent algorithm Back Propagation (BP) neural network algorithm to solve this problem instead of complex traditional mathematics algorithm. Different from normal usage of BP neural network algorithm, we look each nodes in different layers as the each result in solution this time. Mend each node weight constantly by some groups of prior data, we will get the solution which is consist of each node weight. In the experiment simulation, solutions of iteration with 500times and 1000times show that this algorithm is feasible. At the end of the paper, we concluded this experiment and analysis the executable of another intelligent algorithm which will be used in this EIT problem, Genetic algorithm.

关键词： EIT Inverse problem intelligent algorithm Neural network

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Man-algorithm Cooperation intelligent Design of Clothing Products in Multi Links

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FIBRES & TEXTILES IN EASTERN EUROPE 2022年第1期30卷 59-66页

作者： Chen Han Lei Shen Zhang Shaogeng Wang Mingming Tang Ying Jiangnan Univ Sch Design Wuxi 214122 Jiangsu Peoples R China Fudan Univ Sch Comp Sci & Technol Shanghai 200125 Peoples R China Politecn Milan Sch Design I-20158 Milan Italy

The changes in technology have led to a synchronous change in the clothing design method, as well as media and artistic aesthetics in the same period. The intelligence algorithm is constantly increasing its participation in development and production in the clothing industry. In this study, a variety of intelligent algorithms, including the parameterised number state algorithm, Generative Adversarial Networks, and style transfer were introduced into the multi-links of clothing product design and development, such as clothing shape, print pattern, texture craft, product vision, and so on. Men, an innovative clothing design method based on the cooperation of the intelligent algorithm and various human functional roles was constructed. The method improves the efficiency of the multiple links of clothing design, such as generating 10000 printing patterns every 72.12 seconds, and completing the migration of 92.7 frames of the garment process style every second. To a certain extent, this study realizes the scale economy of clothing design and reduces its marginal cost through the unlimited computing power brought about by Moore's law of digital technology, which provides a reference for the exploration of clothing design in the era of industry 4.0.

关键词： intelligent algorithm cooperation clothing design machine learning efficiency

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Analysis of tactical information collection in sports competition based on the intelligent prompt automatic completion algorithm

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JOURNAL OF intelligent & FUZZY SYSTEMS 2018年第3期35卷 2927-2936页

作者： Gu, Deping Shanghai Univ Financial & Econ Dept Phys Educ Shanghai Peoples R China

At this stage, the technical and tactical information acquisition technology has become the key factor to improve the performance of athletes. In this paper, "manual + automation" information collection method was selected. At the same time, in order to improve the speed of information collection, a kind of intelligent prompt automatic completion algorithm was designed and proposed. In the algorithm, the optimization algorithm was improved in view of the poor convergence in the original algorithm, and then the intelligent completion algorithm with more restrictive was further proposed. By collecting the standard document of dynamic standard information and embedding the basic video file, the algorithm was beneficial to the algorithm and automatic completion and intelligent prompt visual features in the video retrieval and analysis. In addition, in terms of technology, the video semantic description was carried out based on the AVI format, and the video retrieval and video analysis based on sports tactical competition were realized, thus providing complete technical support for coaches and athletes in scientific competitions, and improving the level of athletes' skills and tactics.

关键词： intelligent algorithm information acquisition video retrieval

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