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Special Issue "Importance of Machine Intelligence for Construction Material and Structural Engineering Applications"

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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: closed (31 December 2022) | Viewed by 12307

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Special Issue Editors

Dr. Mehran Khan

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Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: hybrid fiber-reinforced concrete; multi-scale fiber-reinforced composites; analytical modelling; sustainable infrastructure; natural fiber concrete for sustainable construction
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Dr. Shazim Memon

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Department of Civil Engineering, School of Engineering, Nazarbayev University, Bldg. 3, Room 3.330, 53 Kabanbay Batyr Ave., Nur-Sultan 010000, Kazakhstan
Interests: sustainable cement based composites; structural functional integrated concrete; thermal energy storage concrete; post-elevated temperature performance of cementitious composites; structural health monitoring; self-sensing concrete; self-healing concrete; 3D concrete printing; energy efficient buildings
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Dr. Junfei Zhang

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School of Civil and Transportation Engineering, Hebei University of Technology, 5340 Xiping Road, Beichen District, Tianjin 300401, China
Interests: 3D printing technology; cementitous composite; machine learning; smart materials; structures intelligent construction
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Dr. Mizan Ahmed

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School of Civil and Mechanical Engineering, Curtin University, Perth, WA 6102, Australia
Interests: steel structures; concrete structures; steel-concrete composite structures; computational mechanics; structural dynamics
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Prof. Dr. Mingli Cao

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Department of Civil Engineering, Dalian University of Technology, Dalian, China
Interests: high-performance fiber-reinforced cementitious composites; micro/nano-modified cement-based materials; multi-scale fiber reinforced cementitious composites; functional porous cement-based materials
Special Issues, Collections and Topics in MDPI journals

Dr. Jincheng Liu

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Guest Editor

School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
Interests: UHPC; high-temperature performance; construction materials; structural engineering; artificial Intelligence

Special Issue Information

Dear Colleagues,

The use of machine intelligence to calculate material/structural properties is gaining popularity in civil engineering. Two types of machine intelligence have been used recently, i.e., the computational one based on soft computing methods and the artificial one based on hard computing techniques. Machine intelligence can be used in structural engineering to detect damages using sensory or visual data and determine their location and extent. The attributes of concrete mix designs can also be predicted using machine intelligence. As a result, the objective of this Special Issue is to present the most recent developments in the civil engineering sector that have been made possible by more advanced machine intelligence approaches. We are delighted to welcome you to contribute to this discussion by presenting your findings on AI applications and advancements in construction material and structural engineering application problems. Applications in construction material and structural engineering are possible topics for studies. Modelling, optimization, control, measurements, analysis, and applications are all possible topics for articles. Potential topics include but are not limited to the following:

Machine learning and deep learning applications for structural engineering
New algorithms used for the optimization of construction materials
Prediction of concrete material properties using computational techniques
Estimation of the structural performance of steel-concrete composite structures

Dr. Mehran Khan
Dr. Shazim Memon
Dr. Junfei Zhang
Dr. Mizan Ahmed
Prof. Dr. Mingli Cao
Dr. Jincheng Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

machine intelligence
artificial intelligence
computational methods
construction materials
structural engineering

Published Papers (8 papers)

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Research

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17 pages, 5663 KiB

Open AccessArticle

A Novel Design Concept of Cemented Paste Backfill (CPB) Materials: Biobjective Optimization Approach by Applying an Evolved Random Forest Model

and

Materials 2022, 15(23), 8298; https://doi.org/10.3390/ma15238298 - 22 Nov 2022

Viewed by 634

Abstract

For cemented paste backfill (CPB), uniaxial compressive strength (UCS) is the key to ensuring the safety of stope construction, and its cost is an important part of the mining cost. However, there are a lack of design methods based on UCS and cost optimization. To address such issues, this study proposes a biobjective optimization approach by applying a novel evolved random forest (RF) model. First, the evolved RF model, based on the beetle search algorithm (BAS), was constructed to predict the UCS of CPB. The consistency between the predicted value and the actual value is high, which proves that the hybrid machine learning model has a good effect on the prediction of the UCS of CPB. Then, considering the linear relationship between the costs and the components of CPB, a mathematical model of the cost is constructed. Finally, based on the weighted sum method, the biobjective optimization process of the UCS and cost of CPB is conducted; the Pareto front optimal solutions of UCS and the cost of CPB can be obtained by the sort of solution set. When the UCS or the cost of CPB is constant, the Pareto front optimal solutions can always have a lower cost or a higher UCS compared with the actual dataset, which proves that the biobjective optimization approach has a good effect. Full article

(This article belongs to the Special Issue Importance of Machine Intelligence for Construction Material and Structural Engineering Applications)

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22 pages, 4897 KiB

Open AccessArticle

Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches

Hisham Jahangir Qureshi

Muhammad Umair Saleem

Muhammad Faisal Javed

Abdulrahman Fahad Al Fuhaid

and

Materials 2022, 15(21), 7412; https://doi.org/10.3390/ma15217412 - 22 Oct 2022

Cited by 3 | Viewed by 1220

Abstract

The use of superabsorbent polymers, sometimes known as SAP, is a tremendously efficacious method for reducing the amount of autogenous shrinkage (AS) that occurs in high-performance concrete. This study utilizes support vector regression (SVR) as a standalone machine-learning algorithm (MLA) which is then ensemble with boosting and bagging approaches to reduce the bias and overfitting issues. In addition, these ensemble methods are optimized with twenty sub-models with varying the n^th estimators to achieve a robust R². Moreover, modified bagging as random forest regression (RFR) is also employed to predict the AS of concrete containing supplementary cementitious materials (SCMs) and SAP. The data for modeling of AS includes water to cement ratio (W/C), water to binder ratio (W/B), cement, silica fume, fly ash, slag, the filer, metakaolin, super absorbent polymer, superplasticizer, super absorbent polymer size, curing time, and super absorbent polymer water intake. Statistical and k-fold validation is used to verify the validation of the data using MAE and RMSE. Furthermore, SHAPLEY analysis is performed on the variables to show the influential parameters. The SVM with AdaBoost and modified bagging (RF) illustrates strong models by delivering R² of approximately 0.95 and 0.98, respectively, as compared to individual SVR models. An enhancement of 67% and 63% in the RF model, while in the case of SVR with AdaBoost, it was 47% and 36%, in RMSE and MAE of both models, respectively, when compared with the standalone SVR model. Thus, the impact of a strong learner can upsurge the efficiency of the model. Full article

(This article belongs to the Special Issue Importance of Machine Intelligence for Construction Material and Structural Engineering Applications)

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20 pages, 2697 KiB

Open AccessArticle

PCA-Based Hybrid Intelligence Models for Estimating the Ultimate Bearing Capacity of Axially Loaded Concrete-Filled Steel Tubes

Kaffayatullah Khan

Rahul Biswas

Jitendra Gudainiyan

Muhammad Nasir Amin

Hisham Jahangir Qureshi

Abdullah Mohammad Abu Arab

and

Mudassir Iqbal

Materials 2022, 15(18), 6477; https://doi.org/10.3390/ma15186477 - 18 Sep 2022

Viewed by 1388

Abstract

In order to forecast the axial load-carrying capacity of concrete-filled steel tubular (CFST) columns using principal component analysis (PCA), this work compares hybrid models of artificial neural networks (ANNs) and meta-heuristic optimization algorithms (MOAs). In order to create hybrid ANN models, a dataset of 149 experimental tests was initially gathered from the accessible literature. Eight PCA-based hybrid ANNs were created using eight MOAs, including artificial bee colony, ant lion optimization, biogeography-based optimization, differential evolution, genetic algorithm, grey wolf optimizer, moth flame optimization and particle swarm optimization. The created ANNs’ performance was then assessed. With R² ranges between 0.7094 and 0.9667 in the training phase and between 0.6883 and 0.9634 in the testing phase, we discovered that the accuracy of the built hybrid models was good. Based on the outcomes of the experiments, the generated ANN-GWO (hybrid model of ANN and grey wolf optimizer) produced the most accurate predictions in the training and testing phases, respectively, with R² = 0.9667 and 0.9634. The created ANN-GWO may be utilised as a substitute tool to estimate the load-carrying capacity of CFST columns in civil engineering projects according to the experimental findings. Full article

(This article belongs to the Special Issue Importance of Machine Intelligence for Construction Material and Structural Engineering Applications)

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22 pages, 3584 KiB

Open AccessArticle

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

Jitendra Gudainiyan

and

Materials 2022, 15(15), 5242; https://doi.org/10.3390/ma15155242 - 29 Jul 2022

Cited by 6 | Viewed by 966

Abstract

Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The utilization of pozzolanic additives and industrial by-products in concrete and grouting materials has an important role in reducing the Portland cement usage, the CO₂ emissions, and disposal issues. Thus, the goal of the present work is to estimate the compressive strength (CS) of polyethylene terephthalate (PET) and two supplementary cementitious materials (SCMs), namely FA and SF, blended cementitious grouts to produce green mix. For this purpose, five hybrid least-square support vector machine (LSSVM) models were constructed using swarm intelligence algorithms, including particle swarm optimization, grey wolf optimizer, salp swarm algorithm, Harris hawks optimization, and slime mold algorithm. To construct and validate the developed hybrid models, a sum of 156 samples were generated in the lab with varying percentages of PET and SCM. To estimate the CS, five influencing parameters, namely PET, SCM, FLOW, 1-day CS (CS_1D), and 7-day CS (CS_7D), were considered. The performance of the developed models was assessed in terms of multiple performance indices. Based on the results, the proposed LSSVM-PSO (a hybrid model of LSSVM and particle swarm optimization) was determined to be the best performing model with R² = 0.9708, RMSE = 0.0424, and total score = 40 in the validation phase. The results of sensitivity analysis demonstrate that all the input parameters substantially impact the 28-day CS (CS_28D) of cementitious grouts. Among them, the CS_7D has the most significant effect. From the experimental results, it can be deduced that PET/SCM has no detrimental impact on CS_28D of cementitious grouts, making PET a viable alternative for generating sustainable and green concrete. In addition, the proposed LSSVM-PSO model can be utilized as a novel alternative for estimating the CS of cementitious grouts, which will aid engineers during the design phase of civil engineering projects. Full article

(This article belongs to the Special Issue Importance of Machine Intelligence for Construction Material and Structural Engineering Applications)

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21 pages, 4572 KiB

Open AccessArticle

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

Ahmed Farouk Deifalla

Oussama Accouche

and

Marc Azab

Materials 2022, 15(15), 5194; https://doi.org/10.3390/ma15155194 - 27 Jul 2022

Cited by 18 | Viewed by 2174

Abstract

Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R²), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R² of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R² values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete. Full article

(This article belongs to the Special Issue Importance of Machine Intelligence for Construction Material and Structural Engineering Applications)

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19 pages, 4588 KiB

Open AccessEditor’s ChoiceArticle

Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete

Mohanad Muayad Sabri Sabri

Abdelatif Salmi

and

Dina Yehia Zakaria Ewais

Materials 2022, 15(13), 4450; https://doi.org/10.3390/ma15134450 - 24 Jun 2022

Cited by 8 | Viewed by 1505

Abstract

The low tensile strain capacity and brittle nature of high-strength concrete (HSC) can be improved by incorporating steel fibers into it. Steel fibers’ addition in HSC results in bridging behavior which improves its post-cracking behavior, provides cracks arresting and stresses transfer in concrete. Using machine learning (ML) techniques, concrete properties prediction is an effective solution to conserve construction time and cost. Therefore, sophisticated ML approaches are applied in this study to predict the compressive strength of steel fiber reinforced HSC (SFRHSC). To fulfil this purpose, a standalone ML model called Multiple-Layer Perceptron Neural Network (MLPNN) and ensembled ML algorithms named Bagging and Adaptive Boosting (AdaBoost) were employed in this study. The considered parameters were cement content, fly ash content, slag content, silica fume content, nano-silica content, limestone powder content, sand content, coarse aggregate content, maximum aggregate size, water content, super-plasticizer content, steel fiber content, steel fiber diameter, steel fiber length, and curing time. The application of statistical checks, i.e., root mean square error (RMSE), determination coefficient (R²), and mean absolute error (MAE), was also performed for the assessment of algorithms’ performance. The study demonstrated the suitability of the Bagging technique in the prediction of SFRHSC compressive strength. Compared to other models, the Bagging approach was more accurate as it produced higher, i.e., 0.94, R², and lower error values. It was revealed from the SHAP analysis that curing time and super-plasticizer content have the most significant influence on the compressive strength of SFRHSC. The outcomes of this study will be beneficial for researchers in civil engineering for the timely and effective evaluation of SFRHSC compressive strength. Full article

(This article belongs to the Special Issue Importance of Machine Intelligence for Construction Material and Structural Engineering Applications)

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Review

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30 pages, 5711 KiB

Open AccessReview

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

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Materials 2022, 15(21), 7830; https://doi.org/10.3390/ma15217830 - 06 Nov 2022

Cited by 3 | Viewed by 1366

Abstract

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. Full article

(This article belongs to the Special Issue Importance of Machine Intelligence for Construction Material and Structural Engineering Applications)

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23 pages, 4942 KiB

Open AccessReview

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

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Materials 2022, 15(13), 4512; https://doi.org/10.3390/ma15134512 - 27 Jun 2022

Cited by 14 | Viewed by 2211

Abstract

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. Full article

(This article belongs to the Special Issue Importance of Machine Intelligence for Construction Material and Structural Engineering Applications)

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