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Special Issue "Construction Materials and Artificial Intelligence"

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A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".

Deadline for manuscript submissions: 10 January 2024 | Viewed by 4672

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Special Issue Editors

Dr. Han Zhu

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Guest Editor

School of Civil Engineering, Tianjin University, Tianjin 300350, China
Interests: portland cement concrete; crumb rubber; polymer and nano concrete; asphalt and HMA; material property; durability; artificial intelligence; road; bridge and structural application

Prof. Dr. Yasser E. Ibrahim

E-Mail Website
Guest Editor

Engineering Management Department, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia
Interests: sustainable RC structures; seismic-resistant structures; sustainable concrete materials; concrete durability

Special Issue Information

Dear Colleagues,

Concrete is the most widely used building material in construction industries. It is characterized by high strength and good durability. In contrast, normal concrete exhibits poor deformability and low compression toughness, which affects its ability to withstand dynamic loads. These deficiencies limit the application of concrete in various civil engineering structures that endure dynamic loads, such as mechanical platforms and airport pavements. In order to overcome these drawbacks, various admixtures/additives, such as nanomaterials (nanosilica, silica fumes, carbon nanotubes, etc.) crumb rubber, natural and synthetic fibers, and polymer materials, which include polyurethane, epoxy resin polyethylene, etc., have been incorporated into concrete to modify its properties. Moreover, the properties of concrete, such as its mechanical properties, dynamic-load-bearing capabilities, and durability, are improved as a result of these admixtures. However, economical and efficient techniques are required to comprehensively evaluate concrete performance due to the variety in the compositions. Therefore, using artificial intelligent (AI) through the application of soft computing/classical models such as artificial neural network (ANNs), support vector machines (SVMs), multilinear regression (MLR), Adaptive Neuro-fuzzy Inference Systems (ANFISs), Extreme Learning Machines (ELMs), Gaussian regression processes (GPRs), and ensemble models, including Random Forest, XGBoost, etc., are employed.

Dr. Han Zhu
Dr. Yasser E. Ibrahim
Guest Editors

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Keywords

concrete
material property
durability
artificial intelligence
machine learning
admixture
nano, polymer and crumb rubber

Published Papers (4 papers)

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Research

Open AccessArticle

Research on Response Parameters and Classification Identification Method of Concrete Vibration Process

and

Materials 2023, 16(8), 2958; https://doi.org/10.3390/ma16082958 - 07 Apr 2023

Viewed by 730

Abstract

The vibration process applied to fresh concrete is an important link in the construction process, but the lack of effective monitoring and evaluation methods results in the quality of the vibration process being difficult to control and, therefore, the structural quality of the resulting concrete structures difficult to guarantee. In this paper, according to the sensitivity of internal vibrators to vibration acceleration changes under different vibration media, the vibration signals of vibrators in air, concrete mixtures, and reinforced concrete mixtures were collected experimentally. Based on a deep learning algorithm for load recognition of rotating machinery, a multi-scale convolution neural network combined with a self-attention feature fusion mechanism (SE-MCNN) was proposed for medium attribute recognition of concrete vibrators. The model can accurately classify and identify vibrator vibration signals under different working conditions with a recognition accuracy of up to 97%. According to the classification results of the model, the continuous working times of vibrators in different media can be further statistically divided, which provides a new method for accurate quantitative evaluation of the quality of the concrete vibration process. Full article

(This article belongs to the Special Issue Construction Materials and Artificial Intelligence)

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Open AccessArticle

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

Abdulrahman Fahad Al Fuhaid

and

Hani Alanazi

Materials 2023, 16(3), 1277; https://doi.org/10.3390/ma16031277 - 02 Feb 2023

Viewed by 974

Abstract

The chloride diffusion coefficient (Dcl) is one of the most important characteristics of concrete durability. This study aimed to develop a prediction model for the Dcl of concrete incorporating supplemental cementitious material. The datasets of concrete containing supplemental cementitious materials (SCMs) such as tricalcium aluminate (C₃A), ground granulated blast furnace slag (GGBFS), and fly ash were used in developing the model. Five machine learning (ML) algorithms including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) were used in the model development. The performance of the developed models was tested using five evaluation metrics, namely, normalized reference index (RI), coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The SVM models demonstrated the highest prediction accuracy with R² values of 0.955 and 0.951 at the training and testing stage, respectively. The prediction accuracy of the machine learning (ML) algorithm was checked using the Taylor diagram and Boxplot, which confirmed that SVM is the best ML algorithm for estimating Dcl, thus, helpful in establishing reliable tools in concrete durability design. Full article

(This article belongs to the Special Issue Construction Materials and Artificial Intelligence)

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Open AccessArticle

Enhancing Sustainability of Corroded RC Structures: Estimating Steel-to-Concrete Bond Strength with ANN and SVM Algorithms

and

Materials 2022, 15(23), 8295; https://doi.org/10.3390/ma15238295 - 22 Nov 2022

Cited by 8 | Viewed by 1453

Abstract

The bond strength between concrete and corroded steel reinforcement bar is one of the main responsible factors that affect the ultimate load-carrying capacity of reinforced concrete (RC) structures. Therefore, the prediction of accurate bond strength has become an important parameter for the safety measurements of RC structures. However, the analytical models are not enough to estimate the bond strength, as they are built using various assumptions and limited datasets. The machine learning (ML) techniques named artificial neural network (ANN) and support vector machine (SVM) have been used to estimate the bond strength between concrete and corroded steel reinforcement bar. The considered input parameters in this research are the surface area of the specimen, concrete cover, type of reinforcement bars, yield strength of reinforcement bars, concrete compressive strength, diameter of reinforcement bars, bond length, water/cement ratio, and corrosion level of reinforcement bars. These parameters were used to build the ANN and SVM models. The reliability of the developed ANN and SVM models have been compared with twenty analytical models. Moreover, the analyzed results revealed that the precision and efficiency of the ANN and SVM models are higher compared with the analytical models. The radar plot and Taylor diagrams have also been utilized to show the graphical representation of the best-fitted model. The proposed ANN model has the best precision and reliability compared with the SVM model, with a correlation coefficient of 0.99, mean absolute error of 1.091 MPa, and root mean square error of 1.495 MPa. Researchers and designers can apply the developed ANN model to precisely estimate the steel-to-concrete bond strength. Full article

(This article belongs to the Special Issue Construction Materials and Artificial Intelligence)

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Open AccessArticle

Evaluating the Influence of Elevated Temperature on Compressive Strength of Date-Palm-Fiber-Reinforced Concrete Using Response Surface Methodology

Musa Adamu

Yasser E. Ibrahim

and

Hani Alanazi

Materials 2022, 15(22), 8129; https://doi.org/10.3390/ma15228129 - 16 Nov 2022

Cited by 5 | Viewed by 936

Abstract

Due to its availability and affordable processing, date palm fiber (DPF) is among the natural and sustainable fibers used in cementitious composites. Furthermore, DPF is an agricultural, organic, and fibrous material that when subjected to higher temperature can easily degrade and cause reduction in strength. Therefore, the influence of elevated temperatures on the unit weight and strengths of DPF-reinforced concrete needs to be examined. Under this investigation, DPF is used in proportions of 0–3% weight of binder to produce a DPF-reinforced concrete. Silica fume was utilized as a supplemental cementitious material (SCM) in various amounts of 0%, 5%, 10%, and 15% by weight to enhance the heat resistance of the DPF-reinforced concrete. The concrete was then heated to various elevated temperatures for an hour at 200 °C, 400 °C, 600 °C, and 800 °C. After being exposed to high temperatures, the weight loss and the compressive and relative strengths were examined. The weight loss of DPF-reinforced concrete escalated with increments in temperature and DPF content. The compressive and relative strengths of the concrete improved when heated up to 400 °C, irrespective of the DPF and silica fume contents. The heat resistance of the concrete was enhanced with the replacement of up to 10% cement with silica fume when heated to a temperature up to 400 °C, where there were enhancements in compressive and relative strengths. However, at 800 °C, silica fume caused a significant decline in strength. The developed models for predicting the weight loss and the compressive and relative strengths of the DPF-reinforced concrete under high temperature using RSM have a very high degree of correlation and predictability. The models were said to have an average error of less than 6% when validated experimentally. The optimum DPF-reinforced concrete mix under high temperature was achieved by adding 1% DPF by weight of binder materials, replacing 12.14% of the cement using silica fume, and subjecting the concrete to a temperature of 317 °C. The optimization result has a very high desirability of 91.3%. Full article

(This article belongs to the Special Issue Construction Materials and Artificial Intelligence)

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