Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches

Salami, Babatunde Abiodun; Iqbal, Mudassir; Abdulraheem, Abdulazeez; Jalal, Fazal E.; Alimi, Wasiu; Jamal, Arshad; Tafsirojjaman, T.; Liu, Yue; Bardhan, Abidhan

doi:10.1016/j.cemconcomp.2022.104721

Cited by 56 publications

(14 citation statements)

References 67 publications

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“…Water, sand, cement, and foaming agents + aggregates [13] Lime, cement, sand, aluminum powder + energy stimulus [14] Sand, water, cement + mold [15] Sand, water, cement + mold [16] Manufacturing method; equipment Prefabrication or in situ; mortar mixer and foam generator [13] Prefabrication; high pressure autoclave [17] Prefabrication [15] Prefabrication or in situ [16] Compressive strength, MPa <51.18 [18] <12 [14] <40.5 [19] 20-50 [20] Density, kg/m 3  Grouting, thermal insulation: 300-600  Non-load bearing structures: 600-1200 Non-structural elements: 700 [14] Structural and nonstructural elements: 1700-2000 [21] Structural elements: 2400 [22] Lime, cement, sand, aluminum powder + energy stimulus [14] Sand, water, cement + mold [15] Sand, water, cement + mold [16] Manufacturing method; equipment Prefabrication or in situ; mortar mixer and foam generator [13] Prefabrication; high pressure autoclave [17] Prefabrication [15] Prefabrication or in situ [16] Compressive strength, MPa <51.18 [18] <12 [14] <40.5 [19] 20-50 [20] Density, kg/m 3…”

Section: Compositionmentioning

confidence: 99%

Fire Resistance of Foamed Concrete for Discontinuous Partition Filling

Sulik,

Kukfisz,

Dowbysz

et al. 2024

Materials

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Lightweight concrete exhibits many advantages over traditional concrete such as lower density and thermal conductivity and an easier, cheaper, less energy-consuming manufacturing process. In order to extend its applications, there is a need to study its behavior in fire situations. Due to that, the aim of this study was to assess the fire resistance of foam concrete, depending on its thickness and the foaming process applied. Fire resistance was assessed according to EN 1363-1. The results indicate the usefulness of foam concrete in terms of isolating fire temperatures for discontinuous partition filling that are consequently a real alternative to dedicated solutions in the field of passive fire protection. The density of foam concrete was shown to have a large effect on the ability to insulate fire temperatures with a standard material preparation process. It was also noted that changing the method to continuous foam feeding may result in the achievement of similar values while maintaining foam concrete low density.

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Section: Compositionmentioning

confidence: 99%

Fire Resistance of Foamed Concrete for Discontinuous Partition Filling

Sulik,

Kukfisz,

Dowbysz

et al. 2024

Materials

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“…Algorithm Structure of GA-ANN GA originated from computer simulation studies conducted on biological systems is a stochastic global search optimization method [16]. It simulates phenomena such as replication, crossover, and variation occurring in natural selection and inheritance [11]. Through random selection, crossover, and variant manipulation, the initial population is transformed into a group of individuals.…”

Section: Data Collectionmentioning

confidence: 99%

“…The core idea of GA-ANN is that crossing the weights of two good neural networks produces a better neural network. The reason for pleasantly surprised effectiveness of GA is that it does not directly optimize the ANN network structure, which can result in extremely diverse outcomes [11]. The optimization process includes the following steps (Figure 1):…”

Section: Data Collectionmentioning

confidence: 99%

“…One characteristic of GA is its ability to obtain search information using only the objective function of the problem at hand. The objective function is reflected in the evaluation of individual fitness for minimization problems [29], which is shown in Equation (11).…”

Section: Data Collectionmentioning

confidence: 99%

“…Machine learning (ML) is a typical component of AI, which has witnessed significant growth in the materials and engineering frontier, as well as in the field of concrete. Indeed, this data-driven model-based approach has been used for the multiscale characterization and design of different types of concrete engineering materials and structures [11]. Among the different types of ML-based cases, the artificial neural network (ANN) is currently one of the most used methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advancing Concrete Mix Proportion through Hybrid Intelligence: A Multi-Objective Optimization Approach

Chen,

Xu,

Wen

et al. 2023

Materials

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Concrete mixture design has been a key focus in concrete research. This study presents a new method for concrete mixture design by combining artificial neural networks (ANN), genetic algorithms (GA), and Scipy libraries for hybrid intelligent modeling. This method enables the prediction of concrete mechanical properties and the optimization of mix proportions with single or multi-objective goals. The GA is used to optimize the structure and weight parameters of ANN to improve prediction accuracy and generalization ability (R2 > 0.95, RMSE and MAE < 10). Then, the Scipy library combined with GA-ANN is used for the multi-objective optimization of concrete mix proportions to balance the compressive strength and costs of concrete. Moreover, an AI-based concrete mix proportion design system is developed, utilizing a user-friendly GUI to meet specific strength requirements and adapt to practical needs. This system enhances optimization design capabilities and sets the stage for future advancements. Overall, this study focuses on optimizing concrete mixture design using hybrid intelligent modeling and multi-objective optimization, which contributes to providing a novel and practical solution for improving the efficiency and accuracy of concrete mixture design in the construction industry.

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Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Kazemi

2023

Engineering Reports

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Advanced concrete technology is the science of efficient, cost‐effective, and safe design in civil engineering projects. Engineers and concrete designers are generally faced with the slightest change in the conditions or objectives of the project, which makes it challenging to choose the optimal design among several ones. Besides, the experimental examination of all of them requires time and high costs. Hence, an efficient approach is to utilize artificial intelligence (AI) techniques to predict and optimize real‐world problems in concrete technology. Despite the large body of publications in this field, there are few comprehensive surveys that conduct scientometric analysis. This paper provides a state‐of‐the‐art review that lists, summarizes, and categorizes the most widely used machine learning methods, meta‐heuristic algorithms, and hybrid approaches to concrete issues. To this end, 457 publications are considered during the recent decade with a scientometric approach to highlight the annual trend/active journals/top researchers/co‐occurrence of key title words/countries' participation/research hotspots. In addition, AI techniques are classified into distinct clusters using VOSviewer clustering visualization to identify the application scope and their relationship through the link strength. The findings can be a beacon to help researchers utilize AI techniques in future research on advanced concrete technology.

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Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches

Cited by 56 publications

References 67 publications

Fire Resistance of Foamed Concrete for Discontinuous Partition Filling

Fire Resistance of Foamed Concrete for Discontinuous Partition Filling

Advancing Concrete Mix Proportion through Hybrid Intelligence: A Multi-Objective Optimization Approach

Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

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