Hybrid machine learning for predicting strength of sustainable concrete

Pham, Anh-Duc; Ngo, Ngoc-Tri; Nguyen, Quang-Trung; Truong, Ngoc-Son

doi:10.1007/s00500-020-04848-1

Cited by 39 publications

(14 citation statements)

References 36 publications

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“…In material design and engineering, supervised learning models are typically used for making property predictions, which can be used to filter material candidates in lieu of brute force experimentation. For example, supervised learning models have been used to predict the strength of novel concrete formulations that minimize environmental impacts including embodied CO 2 emissions, − to predict glass transition temperature for functionality in biobased and biodegradable PHA-based polymers, to classify mixed plastic waste for high-throughput sorting for improved recycling, , and to characterize natural fiber reinforced composites to reduce use of synthetic materials . They can also be coupled to genetic algorithms, which mimic evolutionary rules for optimizing objective functions, to optimize the properties of designed materials through design–prediction–evolution iterations …”

Section: Computational Methods For Materiomics and Sustainable Materi...mentioning

confidence: 99%

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics

et al. 2023

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Engineered materials are ubiquitous throughout society and are critical to the development of modern technology, yet many current material systems are inexorably tied to widespread deterioration of ecological processes. Next-generation material systems can address goals of environmental sustainability by providing alternatives to fossil fuel-based materials and by reducing destructive extraction processes, energy costs, and accumulation of solid waste. However, development of sustainable materials faces several key challenges including investigation, processing, and architecting of new feedstocks that are often relatively mechanically weak, complex, and difficult to characterize or standardize. In this review paper, we outline a framework for examining sustainability in material systems and discuss how recent developments in modeling, machine learning, and other computational tools can aid the discovery of novel sustainable materials. We consider these through the lens of materiomics, an approach that considers material systems holistically by incorporating perspectives of all relevant scales, beginning with first-principles approaches and extending through the macroscale to consider sustainable material design from the bottom-up. We follow with an examination of how computational methods are currently applied to select examples of sustainable material development, with particular emphasis on bioinspired and biobased materials, and conclude with perspectives on opportunities and open challenges.

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Section: Computational Methods For Materiomics and Sustainable Materi...mentioning

confidence: 99%

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics

et al. 2023

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“…AI can accurately map relationships between inputs and outputs to foresee the RAC mechanical characteristics. Adopting such prediction mechanisms can save the time and cost of experimental effort wasted to accomplish a predictive RAC strength model (Delgado et al 2020;Pham et al 2020;Yaseen et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review

Ahmed

Ali

2022

ACT

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Recycled aggregate concrete (RAC) has attracted more interesting in the past several years because it is an economical and eco-friendly building material. But generally, the mechanical properties of RAC are poor compared to natural aggregate concrete (NAC). So, the mechanical properties of RAC need robust predictive models to be evaluated before its application. Traditional (empirical based) models, e.g., linear, and non-linear regression methods, have been extensively proposed. But these models lack flexibility in updating (i.e., limited to a finite number of variables) and can give inaccurate results. Consequently, to handle such shortcomings, several Artificial Intelligence (AI) models have been suggested as an alternative strategy for predicting the mechanical properties of RAC. In this study, state-of-the-art AI models were reviewed to predict the mechanical properties of RAC. The application of each predictive model and its training, testing, and performance are critically examined and analysed, consequently identifying present knowledge gaps, practical recommendations, and required future investigation.

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“…Engineers usually use the laboratory test method to study the performance of concrete. However, the laboratory test method has many disadvantages, such as low efficiency and high cost [37][38][39][40][41][42][43]. To find a more efficient and low-cost method to predict the performance of concrete, many researchers choose to use machine learning models to predict the properties of concrete [44][45][46][47][48][49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Design of Construction Materials: A Comparative Study of AI Approaches for Predicting the Strength of Concrete with Blast Furnace Slag

Zhu

Zhou

et al. 2022

Materials

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Concrete production by replacing cement with green materials has been conducted in recent years considering the strategy of sustainable development. This study researched the topic of compressive strength regarding one type of green concrete containing blast furnace slag. Although some researchers have proposed using machine learning models to predict the compressive strength of concrete, few researchers have compared the prediction accuracy of different machine learning models on the compressive strength of concrete. Firstly, the hyperparameters of BP neural network (BPNN), support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor algorithm (KNN), logistic regression (LR), and multiple linear regression (MLR) are tuned by the beetle antennae search algorithm (BAS). Then, the prediction effects of the above seven machine learning models on the compressive strength of concrete are evaluated and compared. The comparison results show that KNN has higher R values and lower RSME values both in the training set and test set; that is, KNN is the best model for predicting the compressive strength of concrete among the seven machine learning models mentioned above.

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Hybrid machine learning for predicting strength of sustainable concrete

Cited by 39 publications

References 36 publications

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics

Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review

Intelligent Design of Construction Materials: A Comparative Study of AI Approaches for Predicting the Strength of Concrete with Blast Furnace Slag

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