A Systematic Review of the Research Development on the Application of Machine Learning for Concrete

Khan, Kaffayatullah; Ahmad, Waqas; Amin, Muhammad; Ahmad, Ayaz

doi:10.3390/ma15134512

Cited by 23 publications

(6 citation statements)

References 111 publications

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“…Citations demonstrate an author’s contribution to a specific area of research [ 61 , 62 , 63 ]. Co-authorship is selected as the basis of analysis in which authors are chosen as the unit of analysis for the collaborative assessment of authors.…”

Section: Results Of Scientometric Analysismentioning

confidence: 99%

Emerging Trends of Ergonomic Risk Assessment in Construction Safety Management: A Scientometric Visualization Analysis

Vijayakumar

Choi

2022

IJERPH

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Ergonomic risk assessment is critical for identifying working posture hazardous to the health of construction workers. Work-related musculoskeletal disorders (WMSDs) are predominant non-fatal injuries in the construction industry owing to manual handling activities and poor working conditions. However, there is a lack of scientific synopsis aiming to better understand the emerging research focus in this field. To fill the research gap, this study performed a scientometric evaluation of the bibliometric data on ergonomic risk assessment from the Web of Science database using VOSviewer software. The purpose of this study is to analyze the co-occurrence network of keywords, co-authorship network, most active countries, and the sources of publication. The results indicate that research related to risk assessment in construction has fluctuating growth, peaking in 2020 with significant advancements in the USA, China, and Canada. WMSDs, risk factors, construction workers, and ergonomics are hot research topics in this field. Furthermore, the research gaps of previous studies and suggestions for future research have been provided to bridge the knowledge gap. We believe that this scientometric review provides useful reference points for early-stage researchers as well as beneficial in-depth information to experienced practitioners and scholars in the construction industry.

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Section: Results Of Scientometric Analysismentioning

confidence: 99%

Emerging Trends of Ergonomic Risk Assessment in Construction Safety Management: A Scientometric Visualization Analysis

Vijayakumar

Choi

2022

IJERPH

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“…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].…”

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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“…However, many of these methods allow for introducing all the elements that influence appropriate evaluation. In addition, the conceptual design, as well as non-invasive measurement, is crucial for processing these data, especially in the current engineering scope [14][15][16][17][18][19]. Therefore, involving machine learning in the analysis is a crucial thing to consider.…”

Section: Introductionmentioning

confidence: 99%

Measurement and Prediction of Effect Slenderness Ratios and Aggregate to the Compressive Strength of Concrete by the Core-Drilling Method in Tandem with Machine Learning

Situmorang¹,

Alisjahbana²,

Riyanto³

et al. 2023

MMEP

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The slenderness ratio, length to diameter, of the cylindrical concrete samples of the slab block by the core-drilling method is believed to affect the compressive strength other than the aggregates in the concrete. In this study, the relationship between the compressive strength with mixing and slenderness ratio of cylindrical concrete specimens was investigated by statistics. Further, the discrimination model for mixing cylindrical concrete specimens has been developed by using machine learning algorithms, including support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and random forest (RF). A total of 180 cylindrical concrete specimens have been measured for compressive strength using UTM. The sample consisted of a mixture of type-A and type-B with a slenderness ratio of 2.48, 2.72, and 3.28, respectively. Samples were obtained by the core-drilling method from slab block concrete. The ANOVA tests showed that the aggregate and slenderness ratio caused a significant difference in the compressive strength of the concrete (p<0.05). This indicates that the type of aggregate mixture in concrete and the slenderness ratio of cylindrical concrete specimens significantly affect the compressive strength of the concrete. The model for discrimination of mixing cylindrical concrete specimens using machine learning algorithms can be used with satisfactory results. LDA is a machine learning algorithm that can show stability in the training and testing stages with accuracy reaching 78% and inconsistency of less than 2.63% (the smallest compared to others). The descending order of machine learning algorithms based on their consistency is LDA > RF > SVM > k-NN. Subsequently, this model can discriminate the aggregate mixture on cylindrical concrete specimens obtained from the core-drilling method.

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A Systematic Review of the Research Development on the Application of Machine Learning for Concrete

Cited by 23 publications

References 111 publications

Emerging Trends of Ergonomic Risk Assessment in Construction Safety Management: A Scientometric Visualization Analysis

Emerging Trends of Ergonomic Risk Assessment in Construction Safety Management: A Scientometric Visualization Analysis

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

Measurement and Prediction of Effect Slenderness Ratios and Aggregate to the Compressive Strength of Concrete by the Core-Drilling Method in Tandem with Machine Learning

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