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Resilience-based optimisation framework for post-earthquake restoration of power systems

RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 257卷

作者： Xie, Qiang Liu, Xiao Wu, Siyuan Tongji Univ Coll Civil Engn Shanghai 200092 Peoples R China

Power systems suffer significant damage during earthquakes and recover slowly. To assess the seismic resilience and optimise the post-earthquake recovery of power systems, this paper proposes a multi-model framework for assessing power systems' performance. The seismic intensity attenuation model was determined based on seismic characteristics. A method for evaluating the operational state of power-system components was developed using seismic vulnerability curves. An operational network model of power systems was created using undirected weighted graphs and adjacency matrices. The power-transmission efficiency, reliability, and power-load capacity of the network model were considered to propose functional metrics for quantitatively assessing the working state of the system. Subsequently, a post-earthquake recovery optimisation framework for power systems was proposed by establishing functional time-varying parameters, curves, and resilience metrics. A four-stage process for analysing recovery-path optimisation was explicitly defined based on seismic resilience metrics., the recovery characteristics and the optimal recovery path were identified by analysing the recovery optimisation of a typical power-network test system. Finally, the seismic retrofitting effects and economic benefits of infrastructure were evaluated, and key components of seismic reinforcement were identified.

关键词： Power system System functional model Post-earthquake recovery path optimisation algorithm Seismic resilience improvement

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Prediction of frost resistance and multiobjective optimisation of low-carbon concrete on the basis of machine learning

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MATERIALS TODAY COMMUNICATIONS 2024年 40卷

作者： Dai, Jinpeng Zhang, Zhijie Dong, Xuwei He, Jie Yang, Xiaoyuan Wang, Qicai Lanzhou Jiaotong Univ Natl & Prov Joint Engn Lab Rd & Bridge Disaster Pr Lanzhou 730070 Peoples R China Jiangsu Res Inst Bldg Sci CO LTD State Key Lab High Preformance Civil Engn Mat Nanjing 210008 Peoples R China Southeast Univ Sch Mat Sci & Engn Nanjing 211189 Peoples R China Lanzhou Jiaotong Univ Civil Engn Dept Lanzhou 730070 Peoples R China Lanzhou Jiaotong Univ Key Lab Optoelect Technol & Intelligent Control Minist Educ Lanzhou 730070 Peoples R China

The use of Portland cement is a major source of carbon dioxide emissions, causing considerable environmental pollution worldwide. Therefore, reducing carbon emissions in concrete is of paramount importance. This study presents a multiobjective optimisation method for low-carbon concrete mix design, combining the use of optimal machine learning models for prediction, with the aim of minimising the cost and carbon emissions of concrete. Several optimisation models, including SVR, CatBoost, RF, XGBoost, DBO-RF and VMD-DBO-RF, were employed to predict the frost resistance of low-carbon concrete. The research findings demonstrate that the VMD-DBO-RF optimisation algorithm achieves high prediction accuracy in terms of RDEM and MLR. For RDEM prediction, R2 = 0.9346, MSE = 0.0508, MAE = 0.1686, and RMSE = 0.2253. For MLR prediction, R2 = 0.9557, MSE = 0.0245, MAE = 0.1042, and RMSE = 0.1565. Furthermore, the model's interpretability was improved by combining SHAP and PDP analyses.

关键词： Low -carbon concrete Machine learning Relative dynamic elastic modulus Mass loss rate optimisation algorithm Mix ratio optimisation design optimisation model

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Prediction of carbon nanotubes reinforced interphase properties in fuzzy fibre reinforced polymer via inverse analysis and optimisation

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COMPUTATIONAL MATERIALS SCIENCE 2024年 231卷

作者： Pawlik, Marzena Le, Huirong Wood, Paul Lu, Yiling Univ Derby Coll Sci & Engn Sch Comp & Engn Markeaton St Derby DE22 3AW England Tsinghua Univ Future Lab Beijing Peoples R China Univ Derby Inst Innovat Sustainable Engn Quaker Way Derby DE1 3HD England

This paper presents the parameter identification procedure of carbon nanotubes (CNTs) reinforced interphase in fuzzy fibre reinforced polymer (FFRP). The procedure was completed with ANSYS Workbench 19.2 software by combining Mechanical ADPL and Goal-Driven optimisation. Firstly, a three-phase representative volume element (RVE) containing carbon fibre, CNTs reinforced interphase and epoxy resin was developed as a collection of Mechanical APDL commands. This RVE model was simulated to evaluate the elastic constants of FFRP lamina. CNTs reinforced interphase was characterised by transversely isotropic model. Interphase prop-erties were parametrised and became input parameters in the Goal-Driven optimisation. FFRP lamina elastic constants were set as the output parameters. Multi-objective Genetic algorithm (MOGA) was used to identify the interphase properties, so that the output FFRP lamina elastic constants match the objective and constraints. The optimisation algorithm converged after 585 evaluations. Five potential candidate point, which met required objectives and constraints, were found. The identified interphase properties agreed well with the literature (an average percentage error of around 2%). This inverse procedure shows the potential to identify the interphase properties in nano-engineered composites, which are extremely difficult to measure experimentally.

关键词： Fuzzy fibre reinforced polymer Nano-reinforced interphase Inverse problem Parameters' identification optimisation algorithm

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Development of a multi-fidelity optimisation strategy based on hybrid methods

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2024年第9期67卷 163页

作者： Ko, Agusti Porta Horcas, Sergio Gonzalez Pons-Prats, Jordi Bugeda, Gabriel Univ Politecn Catalunya BarcelonaTech UPC Dept Civil & Environm Engn DECA C Gran CapitaS-NEdif C1 Campus Nord UPC Barcelona 08034 Spain Univ Politecn Catalunya BarcelonaTech UPC Dept Phys FIS Campus Baix LlobregatC Esteve Terrades 5 Castelldefels 08860 Spain CIMNE Aerosp Engn Grp C Gran CapitaS-NEdif C1 Campus Nord UPC Barcelona 08034 Spain

A multi-fidelity optimisation strategy has been developed in the present work, and its performance is illustrated through a series of test cases. The strategy is based on hybrid methods such that two genetic optimisation algorithms are employed, each coupled to a different fidelity level with transfer of information between them. The aim is that the low fidelity model, being less accurate but with a lower computational cost, performs a comprehensive search along the design space guiding the high fidelity model to the optimum region. This strategy has been shown to reduce the computational time of an optimisation through analytical test cases as well as numerical cases. The analytical cases have been used to tune the parameters that define the multi-fidelity strategy, while the numerical cases are employed to apply the method to engineering problems, focusing on the aerodynamic performance of an airfoil. The speed-up shows a certain dependency to the models relation, both regarding their similarity level as well as the relative computational cost. For cases exhibiting a significant dissimilarity between models, wherein the low fidelity model is notably inaccurate, the attained speed-up diminishes, and numerous instances demonstrate an absence of speed-up. However, for most cases, even with poor model similarity the optimisations are accelerated by an order of 2, while values up to 3-5 were found for higher similarity levels. Hence, the developed strategy shows a relevant decrease of computational cost of an optimisation procedure although its performance is affected by the models relative accuracy.

关键词： optimisation algorithm Multi-fidelity Airfoil aerodynamics Genetic algorithms Hybrid methods CFD

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optimisation enabled deep learning model for traffic sign recognition

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INTERNATIONAL JOURNAL OF HEAVY VEHICLE SYSTEMS 2025年第1期32卷 80-100页

作者： Salim, Soja Jayasudha, J. S. Soniya, B. APJ Abdul Kalam Technol Univ Sree Chitra Thirunal Coll Engn Comp Sci & Engn Trivandrum 695018 India Cent Univ Kerala Comp Sci Kasargod 671316 Kerala India

In this paper, a novel optimisation algorithm-trained deep learning network is developed to recognise all symbol-based traffic signs. The deep learning method employed here for the recognition is deep Q network (DQN) with an optimisation algorithm, named gradient descent-teamwork optimisation algorithm (GD-TOA), for training the DQN. Initially, the Gaussian filter is utilised for the pre-processing phase, in which the noise can be eliminated. Thereafter, the sign localisation is performed using SegU-Net with a modified loss function that includes balance cross-entropy, rescaled hinge loss, and insensitive loss to localise the sign region from the image. Finally, TSR is performed using the proposed GD-TOA-based DQN so as to improve the TSR performance. The experimentation reveals that the proposed GD-TOA-based DQN technique attains an overall improvement regarding the precision, accuracy, recall, and F-measure with values 0.977, 0.972, 0.971, and 0.973, respectively for TSR.

关键词： traffic safety TSR traffic sign recognition deep learning sign localisation optimisation algorithm

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The optimisation design of CFRP bumper beam based on ply compatibility

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INTERNATIONAL JOURNAL OF VEHICLE DESIGN 2024年第1-2期94卷 100-122页

作者： Chen, Jing Xu, Sen Liu, Zhen Tang, Aotian Lv, Wei Jilin Univ State Key Lab Automobile Simulat & Control ASCL Changchun 130022 Jilin Peoples R China China FAW Corp Ltd Commercial Vehicle Dev Inst Jiefang Business Div Changchun 130011 Jilin Peoples R China

In recent years, the global car ownership has continued to grow. In order to reduce the energy consumption rate of automobiles and improve the driving dynamics of automobiles, automobile lightweight technology has been widely used in the automobile industry. At the same time, with the continuous development of new material technology, various low-density and high-strength materials (aluminium alloy, high-strength steel, carbon fibre, basalt fibre, glass fibre, etc.) are gradually being used in some parts of automobiles. The purpose of this paper is to carry out the lightweight and optimal design of the carbon fibre reinforced polymer front bumper beam of the automobile by optimising the ply design of the carbon fibre reinforced polymer bumper beam based on the compatibility of the laminate layup. Compared with the traditional steel bumper beam, it has finally achieved 47.61% weight reduction while ensuring the collision performance.

关键词： bumper beam carbon fibre reinforced polymer ply compatibility lightweight design optimisation algorithm

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An image encryption using hybrid grey wolf optimisation and chaotic map

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INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTER SECURITY 2024年第3-4期24卷 188-213页

作者： Abdul-Kareem, Ali Akram Al-Jawher, Waleed Ameen Mahmoud Informat Inst Postgrad Studies Iraqi Commiss Comp & Informat Baghdad Iraq Uruk Univ Baghdad Iraq

Image encryption is a critical and attractive issue in digital image processing that has gained approval and interest of many researchers in the world. A proposed hybrid encryption method was implemented by using the combination of the Nahrain chaotic map with a well-known optimised algorithm namely the grey wolf optimisation (GWO). It was noted from analysing the results of the experiments conducted on the new hybrid algorithm, that it gave strong resistance against expected statistical invasion as well as brute force. Several statistical analyses were carried out and showed that the average entropy of the encrypted images is near to its ideal information entropy.

关键词： cryptography optimisation algorithm grey wolf optimisation GWO chaotic system chaos security applications secure communication

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A review of research on intelligent technology in building air conditioning system optimisation

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INTERNATIONAL JOURNAL OF REFRIGERATION 2025年 176卷 205-225页

作者： Li, Bo Yong, Jonathan Chung Ee Yu, Lih Jiun Olugu, Ezutah Udoncy Yang, Xiaoqing Zhang, Zhiming Muhieldeen, Mohammed W. Zhejiang Ind Polytech Coll Sch Architectural Engn Shaoxing Zhejiang Peoples R China UCSI Univ Fac Engn Technol & Built Environm Mech Engn Dept Kuala Lumpur 56000 Malaysia Univ Witwatersrand Sch Mech Ind & Aeronaut Engn Johannesburg South Africa

The Sustainable Development Goals (SDGs) aim to enhance cities and communities more comfortable, safe, and environmentally friendly. The SDGs state that the construction sector should take a low-carbon and sustainable development path. During the operation phase of buildings, the energy consumed by air conditioning systems makes up approximately 22 % of the overall energy consumption of a building. The optimisation of the air conditioning operation control strategy is significant for saving energy and cutting emissions. However, a building air conditioning system is a complex system with multiple parameters, nonlinearity, time variance, and multiple objective values. Traditional air conditioning control methods cannot meet the complex needs of energy saving and comfort improvement in a dynamic environment. Currently, many researchers are studying intelligent technologies for optimising building air conditioning systems. This literature review categorises the relevant articles in this field in recent years, discusses the aspects of optimisation of control methods and the application of intelligent technologies, and focuses on the analysis of the characteristics of various intelligent technologies such as Model Predictive Control, Machine Learning, Deep Learning, and intelligent optimisation algorithms, and their advantages in the domain of optimising control for building air conditioning systems.

关键词： SDGS Multi-objective optimisation Intelligent technology optimisation algorithm Occupancy information

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Thermodynamic Modelling for the Prediction and optimisation of Long-Term Performance in Wooden Structures

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INTERNATIONAL JOURNAL OF HEAT AND TECHNOLOGY 2023年第5期41卷 1341-1348页

作者： Liu, Chunyan Bai, Liyan Inner Mongolia Univ Technol Architecture Dept Hohhot 010051 Peoples R China

Over time, ancient wooden structures undergo challenges in structural integrity and stability, influenced by various environmental conditions such as temperature fluctuations and humidity variations. Maintenance and protection of this invaluable architectural heritage require precise performance predictions and optimisations. While existing research has delved deeply into performance prediction of wooden structures, it predominantly focuses on empirical observations and experimental analyses, with minimal establishment and validation of theoretical models. Moreover, extant methods for prediction and optimisation often overlook the strain variation of wood under different temperatures and the stochastic nature of environmental factors. This study seeks to address this research gap by investigating the relationship between strain in the grain direction of ancient wooden beams and temperature, and by formulating a comprehensive thermodynamic model. Additionally, a novel optimisation approach using the chicken swarm optimisation (CSO) algorithm has been introduced to further refine performance parameters of wooden structures. By integrating the theoretical model with the CSO, this research offers a novel perspective and methodology for the prediction and optimisation of the long-term performance of wooden structures.

关键词： ancient architecture wooden structures thermodynamic model Strain chicken swarm optimisation algorithm performance optimisation

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A Two-Step Method for the Operation Rescheduling Problem at the Railway Marshalling Station

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IET INTELLIGENT TRANSPORT SYSTEMS 2025年第1期19卷

作者： Ma, Liang Guo, Jin Bao, Yuanli Tao, Shumei Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu Sichuan Peoples R China Sichuan Engn Res Ctr Train Operat Control Technol Chengdu Sichuan Peoples R China

Current research on the operation planning problem at the railway marshalling station are mainly dependent on the assumption of correct and unaltered input data. However, due to force majeure disturbances such as train delays, engine breakdown, and so on, the operation plan may easily fail. To increase the robustness of the operation plan, we propose a two-step method for solving the operation rescheduling problem at the marshalling station, the first step is to create a static model in order to maximise the efficiency of the operation plan, and the second step is to reschedule the static plans and unexecuted plan caused by unexpected disturbances. A lexicographic multi-objective model is built to reduce disturbance perturbation while maximising station benefits. An iteration optimisation approach with initial solutions is designed to solve the whole model iteratively, and each sub-model is solved by the heuristic backtracking algorithm with constraint propagation (CPr) mechanism called CPr-HBT. Some real-life cases from Shijiazhuang marshalling station of the China Railway show that the proposed two-step method increases the robustness of the operation plans effectively, rescheduling the failed operation plans takes 60 s at most, and CPr-HBT is more efficient than the other algorithms without CPr or heuristics.

关键词： heuristic backtracking optimisation algorithm optimisation model operation rescheduling railway marshalling station

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