A novel integrated approach of RUNge Kutta optimizer and ANN for estimating compressive strength of self-compacting concrete

Biswas, Rahul; Kumar, Manish; Singh, Raushan Kumar; Alzara, Majed; Sayed, Sabah; Abdelmongy, Mohamed; Yosri, Ahmed M.; Yousef, Saif Eldeen A.S.

doi:10.1016/j.cscm.2023.e02163

Cited by 17 publications

(5 citation statements)

References 37 publications

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“…The concrete formwork is an essential element of construction management since failure to achieve the required concrete strength can result in rework and, as a result, cost and schedule overruns [79]. [55], [58], [66], [67], [72], [73], [88], [96] 9 Supplementary Material rice husk ash [54], [59], [60], [70] 4 Supplementary Material limestone [56], [68] 2 Supplementary Material silicafume [9], [60], [83] 3 Supplementary Material superplasticizer [40], [55], [56], [57], [58], [67], [70], [72], [73], [96] 10 Supplementary Material steel fiber [9], [38], [57], [70], [85], [97] 6 Supplementary Material geopolimer [82], [83] 2 Recycle Material recycle coarse aggregate [58], [63], [68], [84], [85], [91], [94] 7 Recycle Material recycle fine aggregate [63], [84],…”

Section: Construction Methodsmentioning

confidence: 99%

“…Cost Saving [9], [13], [38], [39], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78] 44 2 Time Saving [12], [25], [39], [42], [43], [44], [45], [46], [47],…”

Section: Nomentioning

confidence: 99%

“…Environmental Impact [9], [13], [38], [49], [50], [53], [54], [55], [57], [60], [63], [64], [65], [66], [67], [68], [70], [91], [92], [93], [94], [95], [96], [97] 24…”

Section: Reducementioning

confidence: 99%

See 2 more Smart Citations

Effect of Construction Delays and the Preventive Role of Concrete Works Optimization: Systematic Literature Review

Yudhistira,

Satyarno,

Nugroho

et al. 2024

TEM Journal

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Delays in construction are a widespread global problem, leading to potential cost overruns and legal disputes. Additionally, delays can result in a decline in construction quality and loss of public trust. The aim of this study is to examine the effects of project delays in various regions and the preventive role of optimizing concrete works. Literature review and bibliometric analysis are carried out to determine global research trends. Findings show that optimizing concrete works can provide benefits such as cost savings, time savings, improved quality and safety, and environmental benefits. Optimization of concrete material composition is one of the most examined topics in this field. Based on the findings, construction firms have the potential to attain cost efficiencies while concurrently mitigating carbon emissions.

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Section: Construction Methodsmentioning

confidence: 99%

Section: Nomentioning

confidence: 99%

See 1 more Smart Citation

Effect of Construction Delays and the Preventive Role of Concrete Works Optimization: Systematic Literature Review

Yudhistira,

Satyarno,

Nugroho

et al. 2024

TEM Journal

View full text Add to dashboard Cite

show abstract

“…Machine learning (ML) methods have advanced quickly in recent years, and their theories and techniques have been widely utilized to tackle challenging issues in a variety of engineering and scientific domains [2][3][4][5][6][7][8]. Researchers have been driven to apply ANN models and optimization methods to address a variety of civil engineering issues due to the growth of ML techniques [9]. A popular area of research right now is utilizing ANN to predict the mechanical properties of concrete.…”

Section: Introductionmentioning

confidence: 99%

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

Wang,

Du,

Jia

et al. 2024

Materials

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Due to its exceptional qualities, ultra-high-performance concrete (UHPC) has recently become one of the hottest research areas, although the material’s significant carbon emissions go against the current development trend. In order to lower the carbon emissions of UHPC, this study suggests a machine learning-based strategy for optimizing the mix proportion of UHPC. To accomplish this, an artificial neural network (ANN) is initially applied to develop a prediction model for the compressive strength and slump flow of UHPC. Then, a genetic algorithm (GA) is employed to reduce the carbon emissions of UHPC while taking into account the strength, slump flow, component content, component proportion, and absolute volume of UHPC as constraint conditions. The outcome is then supported by the results of the experiments. In comparison to the experimental results, the research findings show that the ANN model has excellent prediction accuracy with an error of less than 10%. The carbon emissions of UHPC are decreased to 688 kg/m3 after GA optimization, and the effect of optimization is substantial. The machine learning (ML) model can provide theoretical support for the optimization of various aspects of UHPC.

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“…For example, Gao et al [12] ML models, including both traditional single models and new ensemble learning models, to accurately assess and predict the frost resistance development of waste rubber in complex environments. Biswas et al [13] proposed an integrated approach using the Runge-Kutta optimizer and ANN to predict the compressive strength of SCC based on the percentage of supplementary cementitious materials replacing cement. The obtained correlation coefficient (R 2 = 0.92) indicates that machine learning models can accurately estimate the compressive strength of SCC.…”

Section: Introductionmentioning

confidence: 99%

Advancing Concrete Mix Proportion through Hybrid Intelligence: A Multi-Objective Optimization Approach

Chen,

Xu,

Wen

et al. 2023

Materials

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Concrete mixture design has been a key focus in concrete research. This study presents a new method for concrete mixture design by combining artificial neural networks (ANN), genetic algorithms (GA), and Scipy libraries for hybrid intelligent modeling. This method enables the prediction of concrete mechanical properties and the optimization of mix proportions with single or multi-objective goals. The GA is used to optimize the structure and weight parameters of ANN to improve prediction accuracy and generalization ability (R2 > 0.95, RMSE and MAE < 10). Then, the Scipy library combined with GA-ANN is used for the multi-objective optimization of concrete mix proportions to balance the compressive strength and costs of concrete. Moreover, an AI-based concrete mix proportion design system is developed, utilizing a user-friendly GUI to meet specific strength requirements and adapt to practical needs. This system enhances optimization design capabilities and sets the stage for future advancements. Overall, this study focuses on optimizing concrete mixture design using hybrid intelligent modeling and multi-objective optimization, which contributes to providing a novel and practical solution for improving the efficiency and accuracy of concrete mixture design in the construction industry.

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A novel integrated approach of RUNge Kutta optimizer and ANN for estimating compressive strength of self-compacting concrete

Cited by 17 publications

References 37 publications

Effect of Construction Delays and the Preventive Role of Concrete Works Optimization: Systematic Literature Review

Effect of Construction Delays and the Preventive Role of Concrete Works Optimization: Systematic Literature Review

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

Advancing Concrete Mix Proportion through Hybrid Intelligence: A Multi-Objective Optimization Approach

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