Classifying High Strength Concrete Mix Design Methods Using Decision Trees

Alghamdi, Saleh

doi:10.3390/ma15051950

Cited by 9 publications

(5 citation statements)

References 30 publications

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“…For instance, a decision tree based model can accurately classify different mix design methods to determine the proportions of concrete ingredients for the desired strength. 207 A random forest based model was proposed to predict the compressive stress and resistivity of electrically conductive cementitious materials. 208 Classification of the crack modes in concrete using a support vector machine learning model was carried out to identify the damage state of the structure.…”

Section: Application Of Microbially Induced Calcium Carbonate Precipi...mentioning

confidence: 99%

“…In addition, there are many other recent studies that are not directly applied to self-healing but have shown applicable approaches to explore self-healing questions in bacteria and supplementary cementitious materials. For instance, a decision tree based model can accurately classify different mix design methods to determine the proportions of concrete ingredients for the desired strength . A random forest based model was proposed to predict the compressive stress and resistivity of electrically conductive cementitious materials .…”

Section: Autonomous Healing Cementitious Materialsmentioning

confidence: 99%

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Toward Self-Healing Concrete Infrastructure: Review of Experiments and Simulations across Scales

et al. 2023

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Cement and concrete are vital materials used to construct durable habitats and infrastructure that withstand natural and human-caused disasters. Still, concrete cracking imposes enormous repair costs on societies, and excessive cement consumption for repairs contributes to climate change. Therefore, the need for more durable cementitious materials, such as those with self-healing capabilities, has become more urgent. In this review, we present the functioning mechanisms of five different strategies for implementing self-healing capability into cement based materials: (1) autogenous self-healing from ordinary portland cement and supplementary cementitious materials and geopolymers in which defects and cracks are repaired through intrinsic carbonation and crystallization; (2) autonomous self-healing by (a) biomineralization wherein bacteria within the cement produce carbonates, silicates, or phosphates to heal damage, (b) polymer–cement composites in which autonomous self-healing occurs both within the polymer and at the polymer–cement interface, and (c) fibers that inhibit crack propagation, thus allowing autogenous healing mechanisms to be more effective. In all cases, we discuss the self-healing agent and synthesize the state of knowledge on the self-healing mechanism(s). In this review article, the state of computational modeling across nano- to macroscales developed based on experimental data is presented for each self-healing approach. We conclude the review by noting that, although autogenous reactions help repair small cracks, the most fruitful opportunities lay within design strategies for additional components that can migrate into cracks and initiate chemistries that retard crack propagation and generate repair of the cement matrix.

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Section: Application Of Microbially Induced Calcium Carbonate Precipi...mentioning

confidence: 99%

Section: Autonomous Healing Cementitious Materialsmentioning

confidence: 99%

Toward Self-Healing Concrete Infrastructure: Review of Experiments and Simulations across Scales

et al. 2023

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“…[63][64][65][66][67][68][69][70]. Estimations of various characteristics of conventional and advanced concretes, such as durability, thermal characteristics, and mechanical characteristics, have been extensively covered in previous studies [71][72][73][74].…”

Section: Categories Of Machine Learningmentioning

confidence: 99%

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

Khan

Ahmad²,

Amin³

et al. 2022

Materials

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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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“…For example, several studies attempted to create models that accurately predict the properties of construction materials by training on data available in the literature [1][2][3][4]. In addition, many properties of fresh and hardened concrete of many different types have been studied and predicted using ML and deep learning models over the past two decades [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Meanwhile, many studies attempted to increase the predictive power of ML models by introducing more complexities or training them with additional experimental data.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Concrete's Compressive Strength via Artificial Neural Network Trained on Synthetic Data

Alghamdi

2023

Eng. Technol. Appl. Sci. Res.

Self Cite

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Predicting concrete compressive strength using machine learning techniques has attracted the focus of many studies in recent years. Typically, given concrete mix ingredients, a machine learning model is trained on experimental data to predict properties of hardened concrete, such as compressive strength at 28 days. This study used computer-generated mix design data that contained mixed ingredients along with the corresponding theoretical strength of each mix to train a neural network and then test them on real-world experimental data. The developed model was able to predict the compressive strength of concrete specimens at 28 days with an R-value of 0.80. Furthermore, increasing the synthetic dataset increased the performance of the model to a point beyond which it started to decrease. The proposed sustainability-promoting method emphasizes the effectiveness of using synthetic data to train machine learning models that yield insightful predictions with acceptable accuracy.

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Classifying High Strength Concrete Mix Design Methods Using Decision Trees

Cited by 9 publications

References 30 publications

Toward Self-Healing Concrete Infrastructure: Review of Experiments and Simulations across Scales

Toward Self-Healing Concrete Infrastructure: Review of Experiments and Simulations across Scales

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

Prediction of Concrete's Compressive Strength via Artificial Neural Network Trained on Synthetic Data

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