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Reliability Topology Optimization of Collaborative Design for Complex Products Under Uncertainties Basedon the TLBO Algorithm

Zhaoxi Hong,Xiangyu Jiang,Yixiong Feng,Qinyu Tian,Jianrong Tan

Engineering 2023, Volume 22, Issue 3,   Pages 71-81 doi: 10.1016/j.eng.2021.06.027

Abstract: We propose a new reliability topology optimization method based on the reliability-and-optimization decoupledmodel and teaching-learning-based optimization (TLBO) algorithm.The reliability optimization under certain uncertainties and structural topology optimization are processedFinally, the decoupled model is solved using the TLBO algorithm, which is characterized by few parametersThe TLBO algorithm is improved with an adaptive teaching factor for faster convergence rates in the initial

Keywords： Plates structure     Reliability Collaborative topology optimization     Teaching–learning-based optimizationalgorithm     Uncertainty     Collaborative design for product life cycle    

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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 4,   Pages 523-535 doi: 10.1007/s11705-021-2083-5

Abstract: Herein we used seven descriptors based on understanding dissolution behavior to establish two solubilityprediction models by machine learning algorithms.

Keywords： solubility prediction     machine learning     artificial neural network     random decision forests    

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Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1397-1414 doi: 10.1007/s11709-022-0860-y

Abstract: indices considering the severity of the ultimate collapse scenario are proposed using reinforcement learningBy comparing the proposed method and the conventional sensitivity index-based methods, it is demonstratedthat the computational cost is considerably reduced because the reinforcement learning model is trainedBesides, it is proved that the Q values produced by the reinforcement learning agent can make

Keywords： progressive collapse     alternate load path     demolition planning     reinforcement learning     graph embedding    

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Machine learning-based seismic assessment of framed structures with soil-structure interaction

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 2,   Pages 205-223 doi: 10.1007/s11709-022-0909-y

Abstract: The objective of the current study is to propose an expert system framework based on a supervised machinelearning technique (MLT) to predict the seismic performance of low- to mid-rise frame structures consideringThe methodology of the framework is based on examining different MLTs to obtain the highest possibleMoreover, the framework provides recommendations for building component design based on the newly introduced

Keywords： seismic hazard     artificial neural network     soil-structure interaction     seismic analysis    

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Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

Ying Zhou,Shiqiao Meng,Yujie Lou,Qingzhao Kong,

Engineering doi: 10.1016/j.eng.2023.08.011

Abstract: and efficiency of structural response prediction, this study proposes a novel physics-informed deep-learning-based

Keywords： prediction     Physics information informed     Real-time prediction     Earthquake engineering     Data-driven machine learning    

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Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic, Wind Turbine, and Battery Article

Asif Khan, Nadeem Javaid

Engineering 2020, Volume 6, Issue 7,   Pages 812-826 doi: 10.1016/j.eng.2020.06.004

Abstract: The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters forThis paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization (TLBO) named the JLBO algorithm for the optimal unit sizing of a PV–WT–battery hybrid system toThe results obtained from the JLBO algorithm are compared with the original Jaya, TLBO, and genetic algorithms

Keywords： 单位尺寸     独立系统     可再生能源     储能系统     优化     负荷缺电率    

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Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning

Frontiers of Structural and Civil Engineering   Pages 1281-1294 doi: 10.1007/s11709-023-0975-9

Abstract: the curvature modes in concrete structures are analyzed and a method to obtain the curvature modes basedSubsequently, various indices for the damage diagnosis of concrete structures based on the curvaturefor concrete structures is established using an artificial bee colony backpropagation neural network algorithmThe feasibility and accuracy of the model are evaluated based on a case study of a concrete gravity dam

Keywords： hydraulic structure     curvature mode     damage detection     artifical neural network     artificial bee colony    

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Optimal Bidding and Operation of a Power Plant with Solvent-Based Carbon Capture under a CO2 Allowance Market: A Solution with a Reinforcement Learning-Based Sarsa Temporal-Difference Algorithm

Ziang Li,Zhengtao Ding,Meihong Wang

Engineering 2017, Volume 3, Issue 2,   Pages 257-265 doi: 10.1016/J.ENG.2017.02.014

Abstract:

In this paper, a reinforcement learning (RL)-based Sarsa temporal-difference (TD) algorithm is appliedfor a unified bidding and operation strategy for a coal-fired power plant with monoethanolamine (MEA)-basedIn the first case, we show the convergence of the Sarsa TD algorithm and find a deterministic optimaldesigned operation and bidding strategies discussed in most of the relevant literature, the Sarsa TD-basedof the power plant that is using the unified bidding and operation strategy explored by the Sarsa TD algorithm

Keywords： carbon capture     Chemical absorption     CO2 allowance market     Optimal decision-making     Reinforcement learning    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimizationCNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learningIt is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, treeParzen estimator, and sequential model-based algorithm configuration.ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Compressive strength prediction and optimization design of sustainable concrete based on squirrel searchalgorithm-extreme gradient boosting technique

Frontiers of Structural and Civil Engineering   Pages 1310-1325 doi: 10.1007/s11709-023-0997-3

Abstract: algorithms with extreme gradient boosting (XGB) to predict the compressive strength of green concrete basedThe results indicated that the squirrel search algorithm-extreme gradient boosting (SSA-XGB) yielded

Keywords： sustainable concrete     fly ash     slay     extreme gradient boosting technique     squirrel search algorithm    

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Optimization of aero-engine pipeline for avoiding vibration based on length adjustment of straight-line

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 1,   Pages 11-11 doi: 10.1007/s11465-021-0667-x

Abstract: Based on this, the parametric finite element model of the pipeline system is established.The genetic algorithm and the golden section algorithm are selected to solve the optimization model,Based on the analysis of the influence of straight-line segment length on the vibration characteristicsIn addition, the optimization efficiency of the golden section algorithm is remarkably higher than thatof the genetic algorithm for length optimization of a single straight-line segment.

Keywords： length adjustment     spatial pipeline     aero-engine     vibration avoidance optimization     genetic algorithm     golden section algorithm    

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Optimization of multi-objective integrated process planning and scheduling problem using a priority basedoptimization algorithm

Muhammad Farhan AUSAF,Liang GAO,Xinyu LI

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 4,   Pages 392-404 doi: 10.1007/s11465-015-0353-y

Abstract: and scheduling, real world problems cannot be fully captured considering only a single objective for optimizationIn this paper, an optimization algorithm for solving MOIPPS problem is presented.The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize theTo compare the results with other algorithms, a C-matric based method has been used.Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm

Keywords： integrated process planning and scheduling (IPPS)     dispatching rules     priority based optimization algorithm     multi-objective optimization    

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Optimization design of anti-seismic engineering measures for intake tower based on non-dominated sortinggenetic algorithm-II

Frontiers of Structural and Civil Engineering   Pages 1428-1441 doi: 10.1007/s11709-023-0998-2

Abstract: characteristics and dynamic response were first analyzed using the mode decomposition response spectrum method basedThe non-dominated sorting genetic algorithm-II (NSGA-II) was adopted to optimize the anti-seismic scheme

Keywords： intake tower     NSGA-II     mode decomposition response spectrum method     anti-seismic engineering measures     optimization    

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Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 8,   Pages 976-989 doi: 10.1007/s11709-022-0840-2

Abstract: Vibration-based damage detection methods have become widely used because of their advantages over traditionalThis paper presents a new approach to identify the crack depth in steel beam structures based on vibrationanalysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly OptimizationAlgorithm (BOA).Next, data are collected from experimental analysis of cracked beams based on different crack depths

Keywords： damage prediction     ANN     BOA     FEM     experimental modal analysis    

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A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system usingenergy filter algorithm

Aeidapu MAHESH, Kanwarjit Singh SANDHU

Frontiers in Energy 2020, Volume 14, Issue 1,   Pages 139-151 doi: 10.1007/s11708-017-0484-4

Abstract: In this paper, the genetic algorithm (GA) is applied to optimize a grid connected solar photovoltaic(PV)-wind-battery hybrid system using a novel energy filter algorithm.A novel energy filter algorithm for smoothing the power injected into the grid has been proposed.The results of the case study for different cases, with and without employing the energy filter algorithm

Keywords： PV-wind-battery hybrid system     size optimization     genetic algorithm    

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Title Author Date Type Operation

Reliability Topology Optimization of Collaborative Design for Complex Products Under Uncertainties Basedon the TLBO Algorithm

Zhaoxi Hong,Xiangyu Jiang,Yixiong Feng,Qinyu Tian,Jianrong Tan

Journal Article

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Journal Article

Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

Journal Article

Machine learning-based seismic assessment of framed structures with soil-structure interaction

Journal Article

Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

Ying Zhou,Shiqiao Meng,Yujie Lou,Qingzhao Kong,

Journal Article

Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic, Wind Turbine, and Battery

Asif Khan, Nadeem Javaid

Journal Article

Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning

Journal Article

Optimal Bidding and Operation of a Power Plant with Solvent-Based Carbon Capture under a CO2 Allowance Market: A Solution with a Reinforcement Learning-Based Sarsa Temporal-Difference Algorithm

Ziang Li,Zhengtao Ding,Meihong Wang

Journal Article

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article

Compressive strength prediction and optimization design of sustainable concrete based on squirrel searchalgorithm-extreme gradient boosting technique

Journal Article

Optimization of aero-engine pipeline for avoiding vibration based on length adjustment of straight-line

Journal Article

Optimization of multi-objective integrated process planning and scheduling problem using a priority basedoptimization algorithm

Muhammad Farhan AUSAF,Liang GAO,Xinyu LI

Journal Article

Optimization design of anti-seismic engineering measures for intake tower based on non-dominated sortinggenetic algorithm-II

Journal Article

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

Journal Article

A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system usingenergy filter algorithm

Aeidapu MAHESH, Kanwarjit Singh SANDHU

Journal Article