A success history-based adaptive differential evolution optimized support vector regression for estimating plastic viscosity of fresh concrete

Nguyen, Thê-Duong; Tran, Tuyen Quang; Nguyen, Hieu; Hoang, Nhat‐Duc

doi:10.1007/s00366-019-00899-7

Cited by 15 publications

(4 citation statements)

References 54 publications

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“…Introduced by Vapnik [40], support vector machines (SVM) have gained attentions of the academic community and have become a preeminent pattern recognition approach [55,[80][81][82][83][84][85][86][87][88][89][90]. Given a data sample set S drawn from a data universe X U , a hidden target function f: X ⟶ 0, 1 { }, we first create a labeled training dataset D, where D � (x, y)|x ∈ S and y � f(x) .…”

Section: Support Vector Machinementioning

confidence: 99%

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Hoang

Tran

2021

Mathematical Problems in Engineering

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Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of “green space” and “nongreen space”. The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM’s hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite’s spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use.

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Section: Support Vector Machinementioning

confidence: 99%

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Hoang

Tran

2021

Mathematical Problems in Engineering

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“…To forecast the plastic viscosity of concrete, Nguyen et al (2019bNguyen et al ( , 2020b developed an integrated algorithm model based on SVM and L-SHADE (SHADE with Linear Population Reduction) hyperparameter optimization, and the square of the correlation coefficient of the model ultimately achieved 0.81. After then, a further in-depth investigation on the pumpability of fresh concrete was carried out.…”

Section: Work Performancementioning

confidence: 99%

Progress in Artificial Intelligence-based Prediction of Concrete Performance

et al. 2021

ACT

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Artificial intelligence technology has super high-dimensional nonlinear computing capabilities, intelligent comprehensive analysis and judgment functions, and self-learning knowledge reserve expression functions. It can unlock the potential of high-dimensional nonlinearity relation between tangible components and performance indicators when compared to the empirical formula generated from classic statistical approaches. This article summarizes the types of artificial intelligence algorithms used to predict concrete performance, comprehensively sorts out the research progress of artificial intelligence technology in predicting the mechanical properties, work performance, and durability of concrete, and compares and analyzes the effects of algorithm selection, sample data, and model construction on the concrete compressive prediction system. The analysis shows that artificial intelligence technology has obvious advantages in measurement accuracy in predicting concrete performance compared to conventional statistical methods. Multiple algorithms should be used to cross-validate the model prediction findings. For tiny data sets, support vector machines are utilized. Decision tree evolution techniques should be used in algorithm models that require feature optimization or dispersed index prediction. Artificial neural networks can be used to solve different challenges. To improve the prediction model and boost its prediction accuracy, measures such as optimized features, integrated algorithms, hyperparameter optimization, enlarged sample data set, richer data sources, and data pretreatment are proposed.

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“…Secondly, the normalised datasets were input into the introduced ML models, i.e., GBR, DTR, RF, SVR, kNN, and DNN. Detailed information about the ML models can be found in the articles, [17,[39][40][41][42][43][44][45][46][47][48][49][50]. The experimental datasets were randomly split at 8:2.…”

Section: Prediction Proceduresmentioning

confidence: 99%

Machine Learning Application to Eco-Friendly Concrete Design for Decarbonisation

2021

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Cement replacement materials can not only benefit the workability of the concrete but can also improve its compressive strength. Reducing the cement content of concrete can also lower CO2 emissions to mitigate the impact of the construction industry on the environment and improve energy consumption. This paper aims to predict the compressive strength (CS) and embodied carbon (EC) of cement replacement concrete using machine learning (ML) algorithms, i.e., deep neural network (DNN), support vector regression (SVR), gradient boosting regression (GBR), random forest (RF), k-nearest neighbors (kNN), and decision tree regression (DTR). Not only is producing an optimal ML model helpful for predicting accurate results, but it also saves time, energy, and costs, compared to conducting experiments. Firstly, 367 pieces of experimental datasets from the open literature were collected, in which cement was replaced with any of the cementitious materials. Secondly, the datasets were imported into the ML models, whose parameters were tuned by the grid search algorithm (GSA). Then, the prediction performance, the coefficient of determination (R2), the prediction accuracy, and the root mean square error (RMSE) were employed to indicate the prediction ability of the ML models. The results demonstrate that the GBR models perform the best prediction of the CS and EC. The R2 of the GBR models for predicting the CS and EC are 0.946 and 0.999, respectively. Thus, it can be concluded that the GBR models have promising abilities for design assistance in cement replacement concrete. Finally, a sensitivity analysis (SA) was conducted in this paper to analyse the effects of the inputs on the CS and EC of the cement replacement concrete. Pulverised fuel ash (PFA), blast-furnace slag (GGBS), Expanded perlite (EP), and Silica fume (SF) were noticed to affect the CS and EC of cement replacement concrete significantly.

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A success history-based adaptive differential evolution optimized support vector regression for estimating plastic viscosity of fresh concrete

Cited by 15 publications

References 54 publications

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Progress in Artificial Intelligence-based Prediction of Concrete Performance

Machine Learning Application to Eco-Friendly Concrete Design for Decarbonisation

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