Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison

Iftikhar, Bawar; Alih, Sophia C.; Vafaei, Mohammadreza; Elkotb, Mohamed Abdelghany; Shutaywi, Meshal; Javed, Muhammad Faisal; Deebani, Wejdan; Khan, M. Ijaz; Aslam, Fahid

doi:10.1016/j.jclepro.2022.131285

Cited by 66 publications

(13 citation statements)

References 83 publications

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“…With the colossal global growth of greenhouse gas (GHG) emissions, polar ice caps have been melting rapidly in the Antarctic and Arctic, extreme weather events have inflicted economic damage, and climate change effects have been accelerating. The cement and concrete industry contributes substantial GHG emissions [48,49]. Demand for concrete continues to grow worldwide.…”

Section: Research Significancementioning

confidence: 99%

Multi-Objective Optimization of Sustainable Concrete Containing Fly Ash Based on Environmental and Mechanical Considerations

et al. 2022

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Infrastructure design, construction and development experts are making frantic efforts to overcome the overbearing effects of greenhouse gas emissions resulting from the continued dependence on the utilization of conventional cement as a construction material on our planet. The amount of CO2 emitted during cement production, transportation to construction sites, and handling during construction activities to produce concrete is alarming. The present research work is focused on proposing intelligent models for fly ash (FA)-based concrete comprising cement, fine and coarse aggregates (FAg and CAg), FA, and water as mix constituents based on environmental impact (P) considerations in an attempt to foster healthier and greener concrete production and aid the environment. FA as a construction material is discharged as a waste material from power plants in large amounts across the world. Its utilization as a supplementary cement ensures a sustainable waste management mechanism and is beneficial for the environment too; hence, this research work is a multi-objective exercise. Intelligent models are proposed for multiple concrete mixes utilizing FA as a replacement for cement to predict 28-day concrete compressive strength and life cycle assessment (LCA) for cement with FA. The data collected show that the concrete mixes with a higher amount of FA had a lesser impact on the environment, while the environmental impact was higher for those mixes with a higher amount of cement. The models which utilized the learning abilities of ANN (-BP, -GRG, and -GA), GP and EPR showed great speed and robustness with R2 performance indices (SSE) of 0.986 (5.1), 0.983 (5.8), 0.974 (7.0), 0.78 (19.1), and 0.957 (10.1) for Fc, respectively, and 0.994 (2.2), 0.999 (0.8), 0.999 (1.0), 0.999 (0.8), and 1.00 (0.4) for P, respectively. Overall, this shows that ANN-BP outclassed the rest in performance in predicting Fc, while EPR outclassed the others in predicting P. Relative importance analyses conducted on the constituent materials showed that FA had relatively good importance in the concrete mixes. However, closed-form model equations are proposed to optimize the amount of FA and cement that will provide the needed strength levels without jeopardizing the health of the environment.

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Section: Research Significancementioning

confidence: 99%

Multi-Objective Optimization of Sustainable Concrete Containing Fly Ash Based on Environmental and Mechanical Considerations

et al. 2022

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“…Most of the previous studies suggested increasing the number of inputs to include the chemical composition of raw ingredients and the environmental conditions (humidity and temperature). In addition, increasing the number of data samples via further experimental tests might enhance the performance of ML models in terms of real and accurate prediction [52,[103][104][105]. Hence, practical applications of ML in the building sector require further in-depth investigations, in order to propose guidelines for ML's applicability.…”

Section: Discussion and Recommendations For Future Workmentioning

confidence: 99%

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

Khan

Ahmad²,

Amin³

et al. 2022

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Research on the applications of new techniques such as machine learning is advancing rapidly. Machine learning methods are being employed to predict the characteristics of various kinds of concrete such as conventional concrete, recycled aggregate concrete, geopolymer concrete, fiber-reinforced concrete, etc. In this study, a scientometric-based review on machine learning applications for concrete was performed in order to evaluate the crucial characteristics of the literature. Typical review studies are limited in their capacity to link divergent portions of the literature systematically and precisely. Knowledge mapping, co-citation, and co-occurrence are among the most challenging aspects of innovative studies. The Scopus database was chosen for searching for and retrieving the data required to achieve the study’s aims. During the data analysis, the relevant sources of publications, relevant keywords, productive writers based on publications and citations, top articles based on citations received, and regions actively engaged in research into machine learning applications for concrete were identified. The citation, bibliographic, abstract, keyword, funding, and other data from 1367 relevant documents were retrieved and analyzed using the VOSviewer software tool. The application of machine learning in the construction sector will be advantageous in terms of economy, time-saving, and reduced requirement for effort. This study can aid researchers in building joint endeavors and exchanging innovative ideas and methods, due to the statistical and graphical portrayal of participating authors and countries.

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“…By polluting air and water systems, dumping these waste materials in the open ground creates a major environmental threat [ 50 ]. Globally, rice husk is produced by nearly 110 million tons and RHA by 22 million tons [ 51 ]. Rice husk is effectively and extensively used as a fuel in numerous nations for rice paddy milling operations and electricity production facilities [ 52 ].…”

Section: Introductionmentioning

confidence: 99%

Mapping Research Knowledge on Rice Husk Ash Application in Concrete: A Scientometric Review

2022

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This study aimed to carry out a scientometric review of rice husk ash (RHA) concrete to assess the various aspects of the literature. Conventional review studies have limitations in terms of their capacity to connect disparate portions of the literature in a comprehensive and accurate manner. Science mapping, co-occurrence, and co-citation are a few of the most difficult phases of advanced research. The sources with the most articles, co-occurrences of keywords, the most prolific authors in terms of publications and citations, and areas actively involved in RHA concrete research are identified during the analysis. The Scopus database was used to extract bibliometric data for 917 publications that were then analyzed using the VOSviewer (version: 1.6.17) application. This study will benefit academics in establishing joint ventures and sharing innovative ideas and strategies because of the statistical and graphical representation of contributing authors and countries.

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Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison

Cited by 66 publications

References 83 publications

Multi-Objective Optimization of Sustainable Concrete Containing Fly Ash Based on Environmental and Mechanical Considerations

Multi-Objective Optimization of Sustainable Concrete Containing Fly Ash Based on Environmental and Mechanical Considerations

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

Mapping Research Knowledge on Rice Husk Ash Application in Concrete: A Scientometric Review

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