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Evaluation and prediction of slope stability using machine learning approaches

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 4,   Pages 821-833 doi: 10.1007/s11709-021-0742-8

Abstract: In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the

Keywords： slope stability     factor of safety     regression     machine learning     repeated cross-validation    

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Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 183-197 doi: 10.1007/s11705-021-2073-7

Abstract: exploration of the design variable space for such scenarios, an adaptive sampling technique based on machinelearning models has recently been proposed.

Keywords： machine learning     flowsheet simulations     constraints     exploration    

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Big data and machine learning: A roadmap towards smart plants

Frontiers of Engineering Management   Pages 623-639 doi: 10.1007/s42524-022-0218-0

Abstract: advanced data processing, storage and analysis, advanced process control, artificial intelligence and machinelearning, cloud computing, and virtual and augmented reality.Exploitation of the information contained in these data requires the use of advanced machine learning

Keywords： big data     machine learning     artificial intelligence     smart sensor     cyber–physical system     Industry 4.0    

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Machine learning in building energy management: A critical review and future directions

Frontiers of Engineering Management 2022, Volume 9, Issue 2,   Pages 239-256 doi: 10.1007/s42524-021-0181-1

Abstract: Over the past two decades, machine learning (ML) has elicited increasing attention in building energy

Keywords： building energy management     machine learning     integrated framework     knowledge evolution    

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Development of machine learning multi-city model for municipal solid waste generation prediction

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 9, doi: 10.1007/s11783-022-1551-6

Abstract:

● A database of municipal solid waste (MSW) generation in China was established.

Keywords： Municipal solid waste     Machine learning     Multi-cities     Gradient boost regression tree    

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State-of-the-art applications of machine learning in the life cycle of solid waste management

Frontiers of Environmental Science & Engineering doi: 10.1007/s11783-023-1644-x

Abstract:

● State-of-the-art applications of machine learning (ML) in solid waste

Keywords： Machine learning (ML)     Solid waste (SW)     Bibliometrics     SW management     Energy utilization     Life cycle    

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Assessment of different machine learning techniques in predicting the compressive strength of self-compacting

Frontiers of Structural and Civil Engineering   Pages 928-945 doi: 10.1007/s11709-022-0837-x

Abstract: compressive strength of SCC (CS of SCC) can be successfully predicted from mix design and curing age by a machinelearning (ML) technique named the Extreme Gradient Boosting (XGB) algorithm, including non-hybrid andNine ML techniques, such as Linear regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine

Keywords： compressive strength     self-compacting concrete     machine learning techniques     particle swarm optimization    

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Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Frontiers of Structural and Civil Engineering   Pages 347-358 doi: 10.1007/s11709-022-0819-z

Abstract: In the present study, a new image-based machine learning method is used to predict concrete compressiveThese include support-vector machine model and various deep convolutional neural network models, namelyThe images and corresponding compressive strength were then used to train machine learning models toOverall, the present findings validated the use of machine learning models as an efficient means of estimating

Keywords： support vector machine     deep convolutional neural network     microscope     digital image     curing period    

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A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 3, doi: 10.1007/s11783-021-1472-9

Abstract:

• A spectral machine learning approach is proposed for predicting mixed

Keywords： Antibiotic contamination     Spectral detection     Machine learning    

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Application of machine learning technique for predicting and evaluating chloride ingress in concrete

Frontiers of Structural and Civil Engineering   Pages 1153-1169 doi: 10.1007/s11709-022-0830-4

Abstract: The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement steel. Therefore, the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement steel. This research aims at predicting the chloride content in concrete using three hybrid models of gradient boosting (GB), artificial neural network (ANN), and random forest (RF) in combination with particle swarm optimization (PSO). The input variables for modeling include exposure condition, water/binder ratio (W/B), cement content, silica fume, time exposure, and depth of measurement. The results indicate that three models performed well with high accuracy of prediction (R2 ≥ 0.90). Among three hybrid models, the model using GB_PSO achieved the highest prediction accuracy (R2 = 0.9551, RMSE = 0.0327, and MAE = 0.0181). Based on the results of sensitivity analysis using SHapley Additive exPlanation (SHAP) and partial dependence plots 1D (PDP-1D), it was found that the exposure condition and depth of measurement were the two most vital variables affecting the prediction of chloride content. When the number of different exposure conditions is larger than two, the exposure significantly impacted the chloride content of concrete because the chloride ion ingress is affected by both chemical and physical processes. This study provides an insight into the evaluation and prediction of the chloride content of concrete in the marine environment.

Keywords： gradient boosting     random forest     chloride content     concrete     sensitivity analysis.    

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Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

Frontiers of Structural and Civil Engineering   Pages 1249-1266 doi: 10.1007/s11709-022-0858-5

Abstract: Machine learning (ML) approaches are implemented in this study, to predict maximum stress and displacementAdaBoost), extreme gradient boosting (XGBoost), category boosting (CatBoost), and light gradient boosting machine

Keywords： machine learning     gridshell structure     regression     sensitivity analysis     interpretability methods    

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Estimation of optimum design of structural systems via machine learning

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 6,   Pages 1441-1452 doi: 10.1007/s11709-021-0774-0

Abstract: Three different structural engineering designs were investigated to determine optimum design variables, and then to estimate design parameters and the main objective function of designs directly, speedily, and effectively. Two different optimization operations were carried out: One used the harmony search (HS) algorithm, combining different ranges of both HS parameters and iteration with population numbers. The other used an estimation application that was done via artificial neural networks (ANN) to find out the estimated values of parameters. To explore the estimation success of ANN models, different test cases were proposed for the three structural designs. Outcomes of the study suggest that ANN estimation for structures is an effective, successful, and speedy tool to forecast and determine the real optimum results for any design model.

Keywords： optimization     metaheuristic algorithms     harmony search     structural designs     machine learning     artificial    

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Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Article

Houfa Wu,Jianyun Zhang,Zhenxin Bao,Guoqing Wang,Wensheng Wang,Yanqing Yang,Jie Wang

Engineering doi: 10.1016/j.eng.2021.12.014

Abstract: traditional LR-based approach, the accuracy of the runoff modeling in ungauged catchments was improved by the machinelearning algorithms because of the outstanding capability to deal with nonlinear relationships.The performances of different approaches were similar in humid regions, while the advantages of the machinelearning techniques were more evident in arid regions.

Keywords： Parameters estimation     Ungauged catchments     Regionalization scheme     Machine learning algorithms     Soil and    

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Shear stress distribution prediction in symmetric compound channels using data mining and machine learning

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5,   Pages 1097-1109 doi: 10.1007/s11709-020-0634-3

Abstract: A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee

Keywords： compound channel     machine learning     SKM model     shear stress distribution     data mining models    

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Prediction of shield tunneling-induced ground settlement using machine learning techniques

Renpeng CHEN, Pin ZHANG, Huaina WU, Zhiteng WANG, Zhiquan ZHONG

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 6,   Pages 1363-1378 doi: 10.1007/s11709-019-0561-3

Abstract: This study investigates the efficiency and feasibility of six machine learning (ML) algorithms, namelyback-propagation neural network, wavelet neural network, general regression neural network (GRNN), extreme learningmachine, support vector machine and random forest (RF), to predict tunneling-induced settlement.

Keywords： EPB shield     shield tunneling     settlement prediction     machine learning    

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Title Author Date Type Operation

Evaluation and prediction of slope stability using machine learning approaches

Journal Article

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Journal Article

Big data and machine learning: A roadmap towards smart plants

Journal Article

Machine learning in building energy management: A critical review and future directions

Journal Article

Development of machine learning multi-city model for municipal solid waste generation prediction

Journal Article

State-of-the-art applications of machine learning in the life cycle of solid waste management

Journal Article

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting

Journal Article

Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Journal Article

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

Journal Article

Application of machine learning technique for predicting and evaluating chloride ingress in concrete

Journal Article

Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

Journal Article

Estimation of optimum design of structural systems via machine learning

Journal Article

Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization

Houfa Wu,Jianyun Zhang,Zhenxin Bao,Guoqing Wang,Wensheng Wang,Yanqing Yang,Jie Wang

Journal Article

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

Journal Article

Prediction of shield tunneling-induced ground settlement using machine learning techniques

Renpeng CHEN, Pin ZHANG, Huaina WU, Zhiteng WANG, Zhiquan ZHONG

Journal Article