Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

Zhu, Fei; Wu, Xiangping; Zhou, Mengmeng; Sabri, Mohanad Muayad Sabri; Huang, Jiandong

doi:10.3390/ma15113833

Cited by 21 publications

(16 citation statements)

References 54 publications

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“…In the same work, nonlinear or linear relationships between parameters are not considered, thus, ML models could play this role. In work [ 28 ], the combination of AI and optimization methods is used to determine the optimal composition of the mix concerning the cement mortar materials. The study [ 29 ] considers the slurry’s optimal composition and the properties relevant to cement production.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven AI Models within a User-Defined Optimization Objective Function in Cement Production

Manis,

Skoumperdis,

Kioroglou

et al. 2024

Sensors

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This paper explores the energy-intensive cement industry, focusing on a plant in Greece and its mill and kiln unit. The data utilized include manipulated, non-manipulated, and uncontrolled variables. The non-manipulated variables are computed based on the machine learning (ML) models and selected by the minimum value of the normalized root mean square error (NRMSE) across nine (9) methods. In case the distribution of the data displayed in the user interface changes, the user should trigger the retrain of the AI models to ensure their accuracy and robustness. To form the objective function, the expert user should define the desired weight for each manipulated or non-manipulated variable through the user interface (UI), along with its corresponding constraints or target value. The user selects the variables involved in the objective function based on the optimization strategy, and the evaluation is based on the comparison of the optimized and the active value of the objective function. The differential evolution (DE) method optimizes the objective function that is formed by the linear combination of the selected variables. The results indicate that using DE improves the operation of both the cement mill and kiln, yielding a lower objective function value compared to the current values.

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Section: Related Workmentioning

confidence: 99%

Data-Driven AI Models within a User-Defined Optimization Objective Function in Cement Production

Manis,

Skoumperdis,

Kioroglou

et al. 2024

Sensors

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“…To reduce the risk of trapping in local optima, the step size of the beetle can be adjusted by Levy flight and self-inertia weight. The improved BAS has been used in the prediction of concrete properties and the design of concrete mix in past studies [ 105 , 106 , 107 , 108 , 109 , 110 ]. It should be noted that for traditional BAS used in concrete design, the beetle step size is constant or decreases in each iteration.…”

Section: Future Prospectsmentioning

confidence: 99%

Mixture Optimization of Cementitious Materials Using Machine Learning and Metaheuristic Algorithms: State of the Art and Future Prospects

Song¹,

Wang

Li³

et al. 2022

Materials

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The hybrid optimization of modern cementitious materials requires concrete to meet many competing objectives (e.g., mechanical properties, cost, workability, environmental requirements, and durability). This paper reviews the current literature on optimizing mixing ratios using machine learning and metaheuristic optimization algorithms based on past studies on varying methods. In this review, we first discuss the conventional methods for mixing optimization of cementitious materials. Then, the problem expression of hybrid optimization is discussed, including decision variables, constraints, machine learning algorithms for modeling objectives, and metaheuristic optimization algorithms for searching the best mixture ratio. Finally, we explore the development prospects of this field, including, expanding the database by combining field data, considering more influencing variables, and considering more competitive targets in the production of functional cemented materials. In addition, to overcome the limitation of the swarm intelligence-based multi-objective optimization (MOO) algorithm in hybrid optimization, this paper proposes a new MOO algorithm based on individual intelligence (multi-objective beetle antenna search algorithm). The development of computationally efficient robust MOO models will continue to make progress in the field of hybrid optimization. This review is adapted for engineers and researchers who want to optimize the mixture proportions of cementitious materials using machine learning and metaheuristic algorithms.

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“…Concrete is made up of cementitious material, aggregate, water, admixture, and mineral admixture following a certain proportion by uniform mixing, compaction molding, curing hardening, and becoming a kind of artificial stone [1][2][3][4][5]. It is one of the most important civil building materials at present [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Compressive Strength of the Cement-Fly Ash–Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method

et al. 2022

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Concrete is the most widely used material in construction. It has the characteristics of strong plasticity, good economy, high safety, and good durability. As a kind of structural material, concrete must have sufficient strength to resist various loads. At the same time, due to the brittleness of concrete, compressive strength is the most important mechanical property of concrete. To solve the disadvantages of the low efficiency of the traditional concrete compressive strength prediction methods, this study proposes a firefly algorithm (FA) and random forest (RF) hybrid machine-learning method to predict the compressive strength of concrete. First, a database is built based on the data of published articles. The dataset in the database contains eight input variables (cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age) and one output variable (concrete compressive strength). Then, the correlation of the eight input variables was analyzed, and the results showed that there was no high correlation between the input variables; thus, they could be used as input variables to predict the compressive strength of concrete. Next, this study used the FA algorithm to optimize the hyperparameters of RF to obtain better hyperparameters. Finally, we verified that the FA and RF hybrid machine-learning model proposed in this study can predict the compressive strength of concrete with high accuracy by analyzing the R values and RSME values of the training set and test set and comparing the predicted value and actual value of the training set and test machine.

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Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

Cited by 21 publications

References 54 publications

Data-Driven AI Models within a User-Defined Optimization Objective Function in Cement Production

Data-Driven AI Models within a User-Defined Optimization Objective Function in Cement Production

Mixture Optimization of Cementitious Materials Using Machine Learning and Metaheuristic Algorithms: State of the Art and Future Prospects

Predicting the Compressive Strength of the Cement-Fly Ash–Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method

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