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Article

A Comprehensive Study on the Estimation of Concrete Compressive Strength Using Machine Learning Models

by
Yusuf Tahir Altuncı
Vocational School of Technical Sciences, Isparta University of Applied Sciences, Isparta 32260, Turkey
Buildings 2024, 14(12), 3851; https://doi.org/10.3390/buildings14123851
Submission received: 8 November 2024 / Revised: 26 November 2024 / Accepted: 28 November 2024 / Published: 30 November 2024
(This article belongs to the Section Building Materials, and Repair & Renovation)
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Abstract

:
Conducting comprehensive analyses to predict concrete compressive strength is crucial for enhancing safety in field applications and optimizing work processes. There is an extensive body of research in the literature focusing on predicting the mechanical properties of concrete, such as compressive strength. Summarizing the key contributions of these studies will serve as a guide for future research. To this end, this study aims to conduct a scientometric analysis of contributions that utilize machine learning (ML) models for predicting concrete compressive strength, assess these models, and provide insights for developing optimal solutions. Additionally, it seeks to offer researchers comprehensive information on prominent research themes, trends, and gaps in the literature regarding concrete compressive strength prediction. For this purpose, 2319 articles addressing the prediction of concrete compressive strength, published between 2000 and 19 August 2024, were identified through the Scopus Database. Scientometric analyses were conducted using VOSviewer software. The evaluation of relevant studies demonstrates that ML models are frequently used to predict concrete compressive strength. The advantages and limitations of these models are examined, with a particular emphasis on key considerations when working with complex datasets. A comprehensive analysis of ML models and their practical contributions to field applications distinguishes this study from existing research. This study contributes significantly to the literature by examining leading institutions, countries, authors, and sources in the field, synthesizing data, and identifying research areas, gaps, and trends in concrete compressive strength prediction. It establishes a strong foundation for the design of ML-supported, reliable, sustainable, and optimized structural systems in civil engineering, building materials, and the concrete industry.

1. Introduction

With the increase in population, the rising demand for concrete has led to its widespread use in the construction of underground, surface, and water facilities [1]. This material remains indispensable due to parameters such as strength, durability, safety, service life, and cost-effectiveness, which contribute to its popularity as a primary building material [2,3,4,5]. Today, the compressive strength of concrete can be determined through destructive testing methods and can also be predicted using non-destructive testing methods. However, destructive testing is generally performed in a laboratory setting, and is often time-consuming, costly, and impractical [6].
Concrete compressive strength is a fundamental performance characteristic and a key requirement for design applications [7]. However, accurately predicting compressive strength remains a challenge due to numerous influencing factors. Parameters such as the water/cement ratio, aggregate/cement ratio, air content/cement ratio, type of cement, cement dosage, chemical additives, mineral admixtures, type of aggregates, curing time, hydration process, and the age of the concrete complicate the development of robust models for such predictions. These challenges have fueled growing interest in the use of ML models for predicting concrete compressive strength [8]. Predicting concrete compressive strength is particularly critical for field applications [9]. Therefore, this study focuses on exploring the potential of machine learning (ML) methods to predict concrete compressive strength by addressing the growing need for modeling various types of concrete in civil engineering.
In recent years, advances in artificial intelligence (AI) have contributed to the development of new solutions for predicting concrete compressive strength. Numerous studies have predicted concrete compressive strength using various methods, making significant contributions to the literature [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65]. Notably, models that predict concrete compressive strength with high accuracy using datasets, without the need for laboratory tests, have been developed. However, due to the heterogeneous and complex nature of concrete, many limitations remain in this field. In particular, there is a lack of in-depth and comprehensive studies among the existing research.
This study, therefore, aims to identify ML models that provide more accurate predictions of concrete compressive strength, by offering valuable contributions to future research. To this end, studies on the prediction of concrete compressive strength between 2000 and 19 August 2024 were scanned in the Scopus Database. ML methods used for predicting concrete compressive strength were examined in depth, and the most common keywords were identified. After searching for these relevant keywords in the Scopus Database, 6583 related articles were found. After a detailed examination, the most relevant 2319 articles were selected. Subsequently, three scientific measurement analyses—Co-occurrence Analysis, Citation Analysis, and Bibliographic Coupling Analysis—were conducted using VOSviewer 1.6.20 software to identify key trends, research gaps, challenges, and prominent sources, institutions, and countries in the field of concrete compressive strength prediction.
Moreover, the studies critically evaluate the advantages and limitations of ML models for predicting concrete compressive strength that have been examined, with a focus on key considerations when working with complex datasets, and practical recommendations for field applications. This comprehensive analysis, which not only evaluates ML based studies in detail but also offers practical contributions to field applications, distinguishes this study from others in the literature.
The ML models frequently highlighted in this study will enable practitioners to make faster and more efficient predictions. Moreover, significant contributions to sustainability can be achieved through the development of specialized concrete types with the potential to reduce carbon footprints. In conclusion, this study opens avenues for ML-supported applications in civil engineering, building materials, and the concrete industry by facilitating advancements in development, improvement, simulation, planning, sustainability, quality, occupational health, and safety.

2. Methodology

This study aims to identify trends, gaps, the most relevant sources, institutions, authors, and countries by examining research on the prediction of concrete compressive strength using the scientific mapping approach. It also seeks to assist in making strategic decisions that will guide future research. The methodology consists of three primary stages: data collection and selection, choosing the scientific mapping method, and applying the scientometric technique.

2.1. Detection of Keywords

A comprehensive literature review was carried out to identify commonly mentioned words in studies related to the prediction of concrete compressive strength by analyzing the abstract sections of selected manuscripts. Specifically, 52 SCI-indexed articles published after 1 January 2015, each with at least 50 citations as identified through the Web of Science, were examined to uncover these terms. These articles were meticulously analyzed to identify frequently used keywords that appeared more than four times. The resulting keywords are presented in Table 1.

2.2. Detection of Relevant Documents

The following query formulation is written in the advanced search engine of WoS to detect all related manuscripts meeting the specified criteria in the abstract section: ((TITLE-ABS-KEY (concrete) AND TITLE-ABS-KEY (“compressive strength”)) AND ((TITLE-ABS-KEY (predict) OR TITLE-ABS-KEY (estimate) OR TITLE-ABS-KEY (artificial) OR TITLE-ABS-KEY (“machine learning”) OR TITLE-ABS-KEY (“Multiple Linear Regression”) OR TITLE-ABS-KEY (“Support Vector Regression”) OR TITLE-ABS-KEY (“Random Forest”) OR TITLE-ABS-KEY (“Support Vector Machine”) OR TITLE-ABS-KEY (“Artificial intelligence”) OR TITLE-ABS-KEY (ai) OR TITLE-ABS-KEY (“Decision Tree”) OR TITLE-ABS-KEY (“Gene Expression Programming”) OR TITLE-ABS-KEY (“Gradient Boosting Regression”))) AND PUBYEAR > 1999 AND PUBYEAR < 2025).
The manuscripts were filtered to include only SCI and SSCI-indexed publications from 2000 to 19 August 2024, written in English. Additionally, only manuscripts categorized as articles and early-access documents were included to ensure the selection of the most prestigious research. This search resulted in the detection of 6583 manuscripts. Subsequently, the documents were screened to omit irrelevant ones, and 2319 documents remained. The final list of documents was analyzed using VOSviewer software to determine current research trends, identify research gaps, highlight the most commonly used ML method, and provide insights into future research directions. Also, the most relevant journals and authors were identified via VOSviewer software, which facilitates enhanced scholarly connections. The flowchart outlining the methodology of the study is provided in Figure 1.

2.3. The Selection of a Science Mapping Tool

To conduct an in-depth examination of a research topic, selecting an appropriate science mapping tool is essential [66]. Bibliometric and scientometric analyses are widely used methods for scientific mapping. While bibliometric analysis is primarily based on examining the literature, scientometric analysis focuses on mapping the development of research using quantitative data based on the literature [67]. In this study, scientific articles retrieved from the Web of Science database using relevant keywords were analyzed using the VOSviewer software, a tool specifically designed for both bibliometric and scientometric mapping. The combination of these methods enabled a comprehensive understanding of research patterns and trends in the field.

2.4. Bibliometric and Scientometric Techniques

Through bibliometric and scientometric analysis methods, this study examined research on the prediction of concrete compressive strength to identify trends, gaps, methodologies, and the most relevant countries, institutions, and authors in the field. These analyses were conducted using VOSviewer software. To detect the most frequently repeated words in titles and abstracts, a range of analytical methods were employed, including Co-occurrence Analysis, Text-based Mapping, Citation Analysis, Bibliographic Coupling, and Bibliographic Data Analysis. The authors, institutions, and citation counts of the most cited articles were identified, along with the countries that published the highest number of articles on concrete compressive strength prediction. A summary of the scientometric analysis used in this study is presented in Figure 2.

3. Findings

3.1. Density of Publications Concerning Studies on Concrete Compressive Strength Prediction

The earliest studies on the prediction of concrete compressive strength date back to the mid-20th century. Between 2000 and 19 August 2024, a total of 6583 articles were published on this topic. The yearly distribution of the 2319 most popular articles is depicted in Figure 3.
In the graph, the red line represents the cumulative trend, the numbers on the left axis indicate the number of documents published annually, and the numbers on the right axis represent the cumulative total. Upon reviewing the graph, it becomes clear that studies on concrete compressive strength prediction have increased over the years. In 2000, there were 9 articles, and this number rose to 49 by 2014. Therefore, the period from 2000 to 2014 can be considered the initial phase. The years 2015–2019 represent a period of acceleration in terms of the number of articles. In 2015, there were 58 articles, followed by 67 in 2016, 82 in 2017, and 136 in 2019.
The period from 2020 to 2023 marks a significant surge in publication numbers, with a noticeable increase starting in 2020. By 2023, the number of published articles peaked at 455. This indicates a marked increase in interest in the topic, particularly after 2020. It is clear that the number of articles published in 2024 will continue to grow, with 216 articles published between 1 January and 19 August 2024, which does not yet represent the full extent of publications for the year. This growth in publication numbers shows that the topic of concrete compressive strength prediction is receiving increasing attention and that this field is expanding rapidly.
The prediction of concrete compressive strength has become an increasingly important research area, and the rise in publication numbers is likely to continue in order to meet this growing demand [66]. Therefore, performing bibliometric analysis to analyze this rapidly increasing volume of data and obtain guiding insights is essential. Through such analysis, researchers and practitioners will be able to make more informed decisions regarding the prediction of concrete compressive strength.

3.2. Main Research Interests Predicting Compressive Strength

Based on the article data, the most frequently repeated keywords were extensively analyzed using VOSviewer software. During the analysis process, the minimum occurrence threshold for terms was set at 30 to identify the most commonly used keywords [66]. Among the terms that met this criterion, meaningful ones were selected, allowing for detailed evaluations of trends related to the prediction of concrete compressive strength. The relationships between the most frequently repeated keywords concerning the prediction of concrete compressive strength are illustrated in Figure 4.
The colors on the map represent thematic areas where specific keyword groups are concentrated. Green represents the fundamental concepts related to concrete compressive strength; red denotes the general properties of concrete, its durability, and the use of recycled materials; blue signifies applications of AI and ML; yellow highlights the relationship between AI and concrete strength; and purple corresponds to alternative concrete materials and methods such as ANN (Artificial Neural Networks) used in their analysis.
The term ‘concrete’ has been addressed across a wide range of engineering problems and research topics. It shows a strong relationship, particularly with topics like ‘mechanical properties’ and ‘durability’ [68,69,70,71,72,73,74,75,76,77,78,79,80]. Similarly, the term ‘compressive strength’ stands out as one of the most critical performance metrics in concrete studies [81,82,83,84,85,86,87,88,89,90,91,92]. The large nodes for main terms like ‘concrete’ and ‘compressive strength’ being connected to many subtopics indicate that these terms are supported by an extensive literature base in concrete research [93,94,95,96,97,98,99,100,101,102,103,104]. If we assess the connection lines in Figure 4, it is evident that the terms ‘concrete types’ and ‘mechanical properties of concrete’ are directly linked to terms like ‘strength’, ‘compressive strength’, ‘flexural strength’, and ‘shear strength’. This is because different types of concrete exhibit different mechanical performance characteristics [105,106,107,108,109,110,111]. The size and subject areas of the repeated keywords in the articles, categorized by research field, are provided in Table 2.
As shown in Table 2, the subject areas can be categorized into six sections: the mechanical properties of concrete, concrete types, modeling and analysis methods, pozzolanic additives, durability and sustainability, and other topics. These sections clearly illustrate which areas receive more attention in predicting concrete compressive strength and which methods are most frequently used.

3.3. Best Journals on Estimating Concrete Compressive Strength

For journals to be recognized as reputable and authoritative within their field, they must publish articles with a high capacity for citations. However, journals that produce a large number of articles and volumes cannot be considered authoritative in their field unless they are able to increase their citation counts. Therefore, citation analysis is important in quantifying the scientific impact of a study, journal, or researcher.
In this section of the study, the key journals publishing on the prediction of concrete compressive strength were identified through Journal, Document, and Citation analyses. For this purpose, the VOSviewer software was used, with the threshold for the number of documents per source set at 20. Out of 257 sources, 24 journals met the threshold. The mapping of journals based on citations is provided in Figure 5.
The journals in the red group on the map represent those focused on construction materials; the blue group represents journals on sustainable civil engineering; the green group includes journals covering composite materials; and the purple and yellow groups represent journals related to materials science. The ‘Construction and Building Materials’ journal holds a central position and stands out in terms of the number of documents, citations, and total link strength compared to other journals. The journals grouped by reference patterns are provided in Table 3.
The journal with the most articles published (370) and the highest number of citations (20,553) is ‘Construction and Building Materials’. In terms of total link strength, the three most important journals contributing to the field of concrete compressive strength prediction are ‘Construction and Building Materials’, ‘Materials’, and ‘Case Studies in Construction Materials’. Although the ‘Journal of Cleaner Production’ and ‘Journal of Building Engineering’ have published fewer articles and received fewer citations compared to the top three journals, they stand out for their high total link strength. This indicates that the articles published in these journals have a strong interaction with other research in the field.

3.4. Key Researchers

Identifying the leading researchers in the field of concrete compressive strength prediction is of great importance, as they drive innovation and progress in this area. Therefore, in this study, analyses of Author, Document, Citation, Average Citation, and Total Link Strength were conducted to highlight the significance of authors working on concrete compressive strength prediction. To identify the most relevant sources using VOSviewer, a minimum threshold of 10 documents per author was set. Out of 6647 authors, 32 authors met the criteria, as shown in Table 4. The size of published documents by these authors is illustrated in Figure 6.
The colors represent the average years of publications. Authors highlighted in yellow represent those who have published more recent documents, while green, blue, and purple, respectively, indicate authors who contributed relatively earlier to the field, based on the citation delay analysis.
Clusters of different colors represent distinct groups of researchers in the field of concrete compressive strength prediction. For instance, researchers in the red and green groups have significant collaborations. Notably, names like Muhammad Faisal Javed and Muhammad Nasir Amin stand out. The lines in the visualization represent the intensity of collaboration, while researchers located at the node positions have more influence and a broader network. The blue group stands out as a more independent cluster compared to the others. The number of articles, citations, and average citations of the authors related to concrete compressive strength prediction are provided in Table 4.
Table 4 ranks the authors based on the number of articles they have published on the prediction of concrete compressive strength. Amin, Muhammad Nasir is the author with the highest number of articles, while Aslam, Fahid has the most citations. Although Asteris, Panagiotis G. has fewer publications, he holds the highest average citation count.

3.5. Leading Organizations

The number of citations is an important criterion for evaluating the academic impact of a publication. Similarly, institutions that produce highly cited papers are typically leading and recognized academic institutions in their field. However, institutions that publish a large number of papers but receive fewer citations may indicate that their publications have not attracted significant academic attention. Citation analysis is crucial for understanding which institutions are conducting more impactful research and raising awareness in the field. Therefore, in this study, the significance of organizations involved in predicting concrete compressive strength was highlighted through analyses of Organization, Document, Citation, and Total Link Strength. For this purpose, VOSviewer was used to identify the most relevant sources, setting a minimum threshold of 10 documents per organization. Out of 4879 sources, 18 organizations met the criteria. The mapping of organizations publishing documents on the prediction of concrete compressive strength is shown in Figure 7.
The connections visible on the map represent collaborations between different universities or engineering faculties. The lines illustrate the intensity of these collaborations. Universities within the green group (such as University of Transport Technology and Duy Tan University) are engaged in national collaborations, while universities in the red group (such as Instituto Superior Técnico, Portugal, and University of Sulaimani, Iraq) are involved in international collaborations. The yellow and purple groups (such as St. Petersburg Polytechnic University, Russia, and Prince Sattam Bin Abdulaziz University, Saudi Arabia) demonstrate collaborations between geographically distant regions. Organizations were grouped based on Citations, with high (1000+) coded as red, medium (500–999) as orange, and low (<500) as yellow, as shown in Table 5.
Table 5 shows that Prince Sattam Bin Abdulaziz University leads in both the number of articles (34) and citations (2150) in the field of concrete compressive strength prediction. Other highly cited institutions include Duy Tan University, Comsats University, University of Mazandaran, and University of Transport Technology.

3.6. Key Countries

Mapping the countries conducting research on predicting concrete compressive strength contributes to a better understanding of scientific production and interactions, as well as to promoting international collaborations. Therefore, in this study, analyses of Country, Document, Citation, Average Citation, and Total Link Strength were conducted to highlight the significance of countries involved in concrete compressive strength prediction research. For this purpose, VOSviewer was used to identify the most relevant countries, with a minimum threshold of 20 documents per country. Out of 100 countries, 33 countries met the criteria. The mapping of countries publishing documents on the prediction of concrete compressive strength is shown in Figure 8.
The map illustrates the network of scientific collaboration between countries. Notably, countries such as China, United States, and Iran are central to the network and maintain strong connections with many other countries. Countries within the same color group tend to collaborate intensively among themselves. The document and citation volumes by country are presented in Table 6.
Other prominent countries include India, Australia, and Turkey. These countries hold significant positions in terms of citation and publication numbers, although their total link strength is relatively low compared to others. Countries like Saudi Arabia, Pakistan, Vietnam, Iraq, Canada, and Egypt rank in the middle in terms of total link strength and citation counts. Countries such as Taiwan, France, Hong Kong, and Italy have lower total link strength compared to other countries. Finally, countries like Bangladesh, Algeria, and Nigeria contribute less to the scientific literature on concrete compressive strength prediction.

4. Discussion

Concrete compressive strength is the most crucial parameter directly influencing structural safety [112]. The development of new materials and technologies in the construction industry makes it essential to accurately predict concrete compressive strength [113]. To predict this strength, it is necessary to understand the parameters, factors, and relationships that affect concrete’s performance characteristics, such as materials, curing, environment, age, and testing methods [114]. Various empirical formulas (Abrams’ Law, Feret’s Formula, Neville’s Formula) and experimental models (Powers and Brownyard Theorem, Barbarulo’s Formula) are commonly used in the literature for predicting concrete compressive strength [115].
Material parameters directly affecting the mechanical performance of concrete include cement type, dosage, freshness, aggregate type, chemical composition, internal structure, quantity, moisture content, the pH of mixing water, temperature, cleanliness, and the type, amount, and brand of additives used [116]. Additionally, the process of hardening and strength development in fresh concrete is directly related to hydration [117,118]. Therefore, factors such as moisture, temperature, curing method, curing duration, and curing volume are critical curing and environmental factors that directly influence the mechanical performance of concrete [119,120,121]. While the compressive strength of conventional concrete does not change significantly after a 28-day curing period, chemical admixture-based concretes continue to show considerable increases in strength. Furthermore, variations in testing devices, such as brand, model, method, calibration, loading speed, and accuracy, can lead to differing results in compressive strength measurements.
This study, aiming to identify trends and challenges in the prediction of concrete compressive strength, conducted a comprehensive literature review using bibliometric and scientometric analysis methods through VOSviewer.
Concrete compressive strength prediction is a widely researched topic across many countries, with approximately half of the publications originating from China, Iran, United States, India, Australia, and Turkey. The size of the construction industries, investments in R&D, and academic infrastructure in these countries have enabled them to conduct significant research in this field and stand out in the international literature. Between 2000 and 19 August 2024, a total of 6583 articles have been published on the prediction of concrete compressive strength, with the ‘Construction and Building Materials’ journal being the most prominent in terms of both publications and citations.
Upon reviewing the articles within the scope of the study, six main research areas were identified: mechanical properties of concrete, concrete types, modeling and analysis methods, pozzolanic additives, durability and sustainability, and other topics. Each area contributes to enhancing the performance, safety, and sustainability of concrete. Research on the mechanical properties under different mixture ratios, materials, curing conditions, and periods is crucial for material science. Developing special concrete types promotes innovations that allow for selecting the most suitable concrete for specific structures and improving material performance. Modeling and analysis methods are essential for predicting and optimizing compressive strength. Pozzolanic additives have the potential to enhance concrete performance, reduce its carbon footprint, and make it more environmentally friendly. Emerging topics like the use of nanomaterials in concrete and 3D-printed concrete open new doors in durability and sustainability. Some of the recent studies that utilize the most popular models for predicting concrete compressive strength are presented in Table 7.
Upon a thorough review of the literature, it has been found that in many studies on conventional concrete, the primary concrete constituents (water, cement, aggregate) are used as input parameters, while the age of the concrete is used as the output parameter [130,131,132,133,134]. Additionally, for non-traditional concrete types, it has been observed that the input and output parameters are more complex [114,135,136,137,138]. While the final compressive strength of conventional concrete is achieved on the 28th day, this process may vary for special types of concrete. Therefore, ML methods are critical for predicting the compressive strength of non-traditional concretes [8,113,114,139,140,141].
Researchers have generally employed a variety of ML methods to predict concrete compressive strength, including Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), Decision Tree (DT), Gradient Boosting Regression (GBR), ANN for deep learning, Adaptive Neuro Fuzzy Inference System (ANFIS) as a hybrid method, and Gene Expression Programming (GEP) as an evolutionary algorithm [137,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156].
AI, by imitating human intelligence and learning capabilities, integrates these abilities into computer systems [157]. To enable AI models to predict concrete compressive strength, large datasets are required, often consisting of parameters like cement type, cement dosage, water/cement ratio, aggregate type, aggregate amount, additive amount, and curing time. After data processing, AI models are trained and used to predict compressive strength for new concrete mixtures.
MLR is a simple, quick method commonly used for predicting concrete compressive strength [158]. This method models the linear relationship between multiple independent variables affecting concrete strength and the dependent variable (compressive strength). For example, the impact of an increase in cement curing time on concrete compressive strength can be clearly observed [159]. However, MLR cannot model nonlinear and complex relationships, as it is sensitive to outliers.
The RF algorithm is used to predict the compressive strength of complex, nonlinear concrete compositions and to optimize concrete formulations [21,51,68]. Multiple decision trees are independently trained to predict compressive strength, and the predictions are then combined to reduce individual errors, producing a more accurate prediction model. This process requires more computational power and time. Model performance is measured using metrics like Mean Squared Error (MSE) and R2 [160]. Once validated, the model can be used to predict compressive strength for new concrete mixtures.
SVR is another algorithm that predicts concrete compressive strength for nonlinear, complex datasets [154]. It uses kernel functions to capture the intricate properties of concrete components, reducing the risk of overfitting [10]. In this way, it minimizes the risk of overfitting. The model’s performance is measured using performance metrics such as MSE and R2 [160]. Since SVR is a method with low interpretability, it is difficult to understand the impact of concrete components on concrete compressive strength.
In the DT method, rules are created based on the data [161]. Each rule splits the data into nodes and branches, modeling the relationships between factors that affect compressive strength, and determining the impact of concrete parameters on strength [153]. If the DT becomes too branched, overfitting can occur.
GBR, unlike DT, trains multiple decision tree models sequentially to improve the prediction of compressive strength [68,162]. In large datasets, this process can become lengthy, and interpreting the influence of concrete parameters on strength becomes challenging.
ANN is a method that effectively determines the influence of complex, nonlinear concrete components on compressive strength [163]. It consists of three main layers: input layer, hidden layers, and output layer [164]. Performance is measured using MSE and R2 metrics [160]. In large datasets, overfitting and cost increases may occur.
ANFIS is a hybrid model that combines the features of ANN and Fuzzy Logic (FL) [165]. ANN is used for learning, while FL handles nonlinear relationships. Concrete parameters are converted into FL rules, and fuzzy sets are defined for the inputs. The model is optimized using ANFIS, and performance is analyzed using MSE, Mean Absolute Error (MAE), and R2 metrics [166]. This model can predict the effects of complex and nonlinear parameters on strength, though in large datasets, computational costs and processing time may increase. Additionally, understanding which parameters affect compressive strength can become difficult.
GEP is an evolutionary algorithm that uses mathematical models and functions to predict compressive strength [16,134,167]. It handles complex concrete parameters with ease. Concrete parameters are taken as input, mathematical models are created, and these models evolve. The model’s performance is evaluated through a fitness function, and its validation is checked using MSE, MAE, and R2 metrics [166]. The trained models can more accurately predict compressive strength, allowing different concrete mix parameters to be modeled effectively. For GEP to work successfully, parameter tuning is essential, as improper tuning can lead to poor model performance.
The study highlights the strengths and weaknesses of ML methods. For example, models like MLR facilitate the interpretation of simple relationships but are disadvantaged in complex relational scenarios [168]. On the other hand, while ANN is a model that provides high accuracy, it has the disadvantage of high computational cost [169]. A summary of the advantages and disadvantages of ML methods [137,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156] is presented in Table 8.
In addition to the information provided in Table 8, it can be stated that large datasets are required for predicting concrete compressive strength using ML models [15,170,171,172,173,174]. However, in some cases, it may be necessary to use limited datasets. In such cases, uncertainties regarding the reliability of the prediction model may arise [175]. It is expected that future efforts will focus on enriching, diversifying, and deriving datasets in situations where data groups are scarce. Additionally, the applicability of the models can be increased by creating more diverse datasets using materials with different parameters.
Most models for predicting concrete compressive strength are models with low explainability and interpretability. It is expected that, in the future, ML models will be designed not only to provide predictions with sufficient accuracy but also to explain the underlying reasons behind these predictions. These expectations, which will enable the development of more accurate, efficient, and sustainable approaches for concrete compressive strength prediction, will undoubtedly provide opportunities for future research. Additionally, modeling techniques for concrete production that reduce the carbon footprint are expected to be utilized in the near future. Furthermore, 3D technology and nanomaterials are expected to open new research avenues in concrete compressive strength prediction through new algorithms and hybrid models. In this context, the data obtained is highly important for stakeholders. Through this study, stakeholders will make critical decisions in areas such as sustainability, environmental policies, support programs, research funds, etc., which will enable them to make their future research more efficient.

5. Conclusions

The prediction of concrete compressive strength is a topic of significant global interest among researchers. Although many studies have been conducted on this subject, there has not been a comprehensive study that closely examines, summarizes, and monitors the latest developments while providing guidance for future research. Therefore, this study is believed to offer valuable insights for upcoming research in the field of concrete compressive strength prediction.
This study, which includes comprehensive analyses for predicting the compressive strength of concrete, is important in terms of increasing safety in field applications and making work processes more efficient. To this end, the study aims to provide practical contributions to literature reviews and field applications for predicting concrete compressive strength. Additionally, the goal is to identify ML models that can predict concrete compressive strength more accurately and contribute to studies in this area.

5.1. Contributions of the Study

Through an in-depth review of the literature on concrete compressive strength prediction, this study identifies six key research areas: mechanical properties of concrete, concrete types, modeling and analysis techniques, pozzolanic additives, durability, and sustainability. These categories reveal the aspects that receive the most focus in predicting concrete compressive strength and highlight the frequently employed methods. Publications on this subject have been progressively increasing since 2000, with a peak in 2023, and the trend is projected to continue in 2024 and beyond. Notably, China, Iran, the United States, India, Australia, and Türkiye are among the leading contributors in this field, which reflect their active involvement in research related to sustainable building materials, environmental impacts, and artificial intelligence.
Among the authors, Muhammad Nasir Amin from King Faisal University has the highest number of publications, while Fahid Aslam from Prince Sattam bin Abdulaziz University is the most cited author. Both researchers’ affiliations with universities in Saudi Arabia highlight the country’s rapid growth in the construction sector, which has stimulated local research efforts in this area. Furthermore, the journal Construction and Building Materials has emerged as a leading publication in the field, reinforcing its reputation as a prestigious outlet for research on construction technologies.
The study emphasizes the importance of machine learning methods in predicting concrete compressive strength, owing to their capability to model complex relationships effectively. These models offer various advantages, including high accuracy, performance, and simplicity (e.g., ANN, RF, GBR, MLR, DT), and the capacity to model nonlinear relationships (e.g., ANN, SVR, ANFIS). However, challenges such as high computational costs, difficulties in interpretation, and the risk of overfitting persist. Each method has its unique strengths and limitations, and careful selection is crucial depending on the specific requirements of the task.

5.2. Recommendations for Academy and Practice

This study is expected to stimulate new research in the areas of civil engineering, construction materials, and the concrete industry. The key recommendations for advancing both academic research and practical applications are as follows:
  • ML models will establish the foundation for developing more accurate concrete compressive strength prediction models.
  • These models will encourage ongoing improvements to their accuracy and reliability.
  • The study will contribute to enhancing non-destructive testing methods for field applications.
  • By enabling digital simulations of construction projects, it will aid in the effective planning and execution of construction processes.
  • The study will support the prediction of compressive strength for concrete made from environmentally friendly and sustainable materials, such as recycled aggregates, olivine, and wastewater treatment sludge ash, contributing to sustainable construction practices.
  • The development of ML-based quality control and safety methodologies will be a key future application.
  • Datasets used in predicting concrete compressive strength will facilitate further studies on structural strength loss over time and the prediction of reinforcement needs.
  • Future efforts should focus on enriching and diversifying datasets, particularly in cases where data are limited.
  • The prediction models for concrete compressive strength are expected to evolve with time-based modeling techniques.
  • Methods that reduce the carbon footprint in concrete production will likely become central in the near future.
  • Additionally, the incorporation of 3D technology and nanomaterials may create new research avenues, leading to novel algorithms and hybrid models in concrete compressive strength prediction.
  • It is anticipated that ML models will not only provide accurate predictions but will also offer insights into the underlying reasons behind those predictions, enhancing their interpretability.
In conclusion, this study serves as the first comprehensive work to review the scientifically recognized research on predicting concrete compressive strength. It is expected to guide future research in this field and significantly enhance the understanding of prediction methods. Researchers selecting an ML model for concrete compressive strength prediction should consider factors such as model advantages and disadvantages, dataset size, problem complexity, computational resources, and interpretability.

5.3. Limitations of This Study

Despite the significant contributions of this study, certain limitations need to be considered. The Scopus database was chosen for this study due to its extensive coverage and reliability. However, some relevant articles may not have been indexed in the Scopus database. Additionally, at the beginning of the study, 6583 articles were identified, but the number was reduced to 2319 by selecting only those articles focusing on the prediction of concrete compressive strength. This selection process may partially limit the scope of the data obtained.
Moreover, the widespread and effective use of machine learning (ML) in the field of concrete compressive strength prediction began after 2000 [176]. Therefore, the study was restricted to publications between the years 2000 and 19 August 2024. This temporal limitation aimed to provide a deeper analysis of more modern and advanced studies and to better reflect current research trends.
In conclusion, while these limitations do not significantly affect the overall findings of the study, they can serve as a reference point for future research.

Funding

This research received no external funding.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The author declares no conflicts of interest.

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Figure 1. Flowchart describing the methodology of the study.
Figure 1. Flowchart describing the methodology of the study.
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Figure 2. Summary of Bibliometric and Scientometric Analyses.
Figure 2. Summary of Bibliometric and Scientometric Analyses.
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Figure 3. Distribution of Studies on Concrete Compressive Strength Prediction by Year.
Figure 3. Distribution of Studies on Concrete Compressive Strength Prediction by Year.
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Figure 4. Mapping the words repeated in documents.
Figure 4. Mapping the words repeated in documents.
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Figure 5. Mapping of journals according to citations.
Figure 5. Mapping of journals according to citations.
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Figure 6. Size of published documents by authors.
Figure 6. Size of published documents by authors.
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Figure 7. Mapping of organizations publishing documents on the prediction of concrete compressive strength.
Figure 7. Mapping of organizations publishing documents on the prediction of concrete compressive strength.
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Figure 8. Mapping of countries publishing documents.
Figure 8. Mapping of countries publishing documents.
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Table 1. The common words detected in abstract sections of manuscripts.
Table 1. The common words detected in abstract sections of manuscripts.
KeywordsNumber of Manuscript Containing Key Words
Compressive strength52
Concrete52
Predict52
Artificial42
ANN32
Machine Learning25
Adaptive Neuro Fuzzy Inference Systems10
Multiple Linear Regression9
Estimate8
Support Vector Regression7
Random Forest6
Support Vector Machine6
AI5
Artificial intelligence5
Decision Tree5
Gene Expression Programming5
Gradient Boosting Regression5
Table 2. Grouping of repeated words in articles according to research field.
Table 2. Grouping of repeated words in articles according to research field.
IDKeywordsSubject
Areas		OccurrencesRate
(%)		Total Link StrengthColor Code
1Compressive strengthMechanical properties of concrete64136.00699Green
2Mechanical properties11077Red
3Strength4948Red
4Flexural strength3959Green
5Concrete compressive strength3720Yellow
6Shear strength3625Red
7Bond strength3230Red
8ConcreteConcrete types28520.82355Red
9Self-compacting concrete6389Green
10Recycled aggregate concrete4855Red
11Reinforced concrete4128Red
12Geopolymer concrete3968Green
13Lightweight concrete3631Green
14High-strength concrete3431Red
15Machine learningModeling and Analysis Methods28131.92368Blue
16Artificial neural network156186Red
17Prediction72127Purple
18Artificial neural networks6588Red
19Gene expression programming6294Blue
20Modeling4872Blue
21Artificial intelligence4680Yellow
22Sensitivity analysis4155Blue
23Ann3539Purple
24Random forest3146Blue
25Fly ashPozzolanic additives1005.00147Green
26Silica füme3152Green
27DurabilityDurability and sustainability473.1345Red
28Sustainability3550Yellow
29ConfinementOther topics473.1331Red
30Ultrasonic pulse velocity3545Yellow
Table 3. Journals grouped by reference patterns.
Table 3. Journals grouped by reference patterns.
IDJournalsDocumentsCitationsTotal Link Strength
1Construction and Building Materails37020,5531781
2Materials1223565851
3Case Studies in Construction Materials811608582
4Journal of Building Engineering992841549
5Journal of Cleaner Production362252501
6Applied Sciences451296343
7Buildings54701309
8Structural Concrete56816290
9Neural Computing and Applications261480280
10Structures731048253
11Advances in Civil Engineering31856242
12Materials Today Communications21313240
13Engineering Structures893565232
14Sustainability38690227
15Scientific Reports26250169
16Cement and Concrete Research212949158
17Journal of Materials in Civil Engineering571669157
18Arabian Journal for Science and Engineering23310145
19European Journal of Environmental and Civil Engineering22424128
20Computers and Concrete, an International Journal52822113
21Cement and Concrete Composites261893108
22Composite Structures23111876
23Magazine of Concrete Research2947356
24Materials and Structures3493048
Table 4. Author citations and article information.
Table 4. Author citations and article information.
IDAuthorDocumentsCitationsAverage CitiationsTotal Link Strength
1Amin, Muhammad Nasir3685923.8696
2Javed, Muhammad Faisal35169248.3465
3Khan, Kaffayatullah35102929.4092
4Aslam, Fahid24201984.1362
5Ahmad, Ayaz21118856.5750
6Ahmad, Waqas2191943.7656
7Nematzadeh, Mahdi2064032.000
8Ly, Hai-Bang1898154.500
9Behnood, Ali17110965.249
10Kurda, Rawaz1770141.2412
11Nehdi, Moncef L.1775244.242
12Alabduljabbar, Hisham1669743.5632
13Farooq, Furqan16151594.6935
14Alyousef, Rayed15110073.3331
15Asteris, Panagiotis G.141828130.578
16Mohammed, Ahmed Salih1445832.7113
17Althoey, Fadi1319615.0820
18Iqbal, Mudassir1326120.0827
19Golafshani, Emadaldin Mohammadi1250341.929
20Deifalla, Ahmed Farouk1122220.1814
21Gamil, Yaser11817.3615
22Huang, Jiandong1118516.821
23Hussain, Qudeer1119818.0010
24Joyklad, Panuwat1151246.5521
25Samui, Pijush1168362.093
26Yang, Keun-Hyeok11999.000
27Ahmed, Hemn Unis1053053.0012
28Ali, Mujahid1014714.7018
29Bahrami, Alireza10898.904
30Mohammed, Azad A.1045945.908
31Salami, Babatunde Abiodun1021321.3016
32Sihag, Parveen1039039.005
Table 5. Grouping of organizations according to Citations.
Table 5. Grouping of organizations according to Citations.
IDOrganizationDocumentsCitationsTotal Link StrengthCitation Color Group
1Department of Civil Engineering, College of Engineering İn Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia34215026Red
2Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa, 31982, Saudi Arabia2745625Yellow
3Department of Civil Engineering, Comsats University Islamabad, Abbottabad, 22060, Pakistan2579026Orange
4Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam24181510Red
5Department of Civil Engineering, Comsats University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan21116413Red
6Department of Civil Engineering, University of Mazandaran, Babolsar, Iran1610040Red
7Civil Engineering Department, College of Engineering, University of Sulaimani, Iraq1556617Orange
8University of Transport Technology, Hanoi, 100000, Vietnam159325Orange
9Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia1416415Yellow
10Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam137725Orange
11Department of Highway and Bridge Engineering, Technical Engineering College, Erbil Polytechnic University, Erbil, 44001, Iraq1252325Orange
12Cerıs, Civil Engineering, Architecture and Georresources Department, Instituto Superior Técnico, Universidade De Lisboa, Av. Rovisco Pais, Lisbon, 1049-001, Portugal1143823Yellow
13Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25120, Pakistan111535Yellow
14Peter the Great St. Petersburg Polytechnic University, St. Petersburg, 195251, Russian Federation113313Yellow
15School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China114410Yellow
16Department of Civil Engineering, College of Engineering, Nawroz University, Duhok, 42001, Iraq1045320Yellow
17School of Civil Engineering, Guangzhou University, Guangzhou, 510006, China101432Yellow
18School of Civil Engineering, Southeast University, Nanjing, 211189, China106640Orange
Table 6. Document and citation volumes by country.
Table 6. Document and citation volumes by country.
IDCountryDocumentsCitationsTotal Link StrengthColor Code
1China65916,848404Purple
2Iran27011,289199Jade
3United States2439484207Red
4India2055195123Aqua
5Australia1797548205Jade
6Turkey177878162Red
7Saudi Arabia1694860356Pink
8Pakistan1514957314Orange
9South Korea138468787Yellow
10Viet Nam1034865117Green
11Iraq913376115Dark purple
12Canada89372396Red
13Egypt871727170Pink
14United Kingdom78477399Blue
15Malaysia772977162Lilac
16Portugal57257351Dark purple
17Poland562113114Ornage
18Taiwan47330315Green
19Russian Federation43115782gray
20Spain40136140Blue
21France3974532Yellow
22Hong Kong39156243Purple
23Italy39122538Blue
24Japan35128937Green
25Greece31226550Aqua
26Thailand31101940Orange
27Germany30100643Blue
28Jordan2850316Green
29Sweden2838668Lilac
30Singapore27103821Purple
31Nigeria2336633Colorless
32Algeria2271225Colorless
33Bangladesh2249226Red
Table 7. Some recent studies using ML methods.
Table 7. Some recent studies using ML methods.
MethodCompositeParameterReference
LRCement stabilized
clayey soil		Soil particles, water, cement, and time.[122]
RFSustainable
Mortar		Gypsum, cement, fly ash, sand 1, sand 2, fibers, superplasticizer, and paraffin.[123]
SVRCustom concreteWater/cement, cement, coarse gravel, fine gravel, san, ultrasonic pulse velocity, rebound hammer number.[124]
DTUltra high
performance
concrete		Cement, sand/cement, silica fume/cement ratio, fly ash/cement, steel fbre/cement, quartz powder/cement ratio, water/cement and admixture/cement.[125]
GBRSelf compacting
Concrete		Cement, water, superplasticizer, maximum spread diameter, fly ash, silica fume, coarse aggregate, fine aggregate, and age.[126]
ANNCustom concreteWater, cement, coarse aggregate, blast furnace slag, age, superplasticizer, fly ash, and fine aggregate.[127]
ANFISHeat cured
geopolymer 		NaOH, Na2SiO3, curing temperature, superplasticizer, ground granulated blast furnace slag, and fly ash.[128]
GEPGeopolymer
Concrete		Fine aggregate, coarse aggregate, NaOH, Na2SiO3, temperature, superplasticizer, ground granulated blast furnace slag, and fly ash.[129]
Table 8. The summary of the advantages and disadvantages of ML methods.
Table 8. The summary of the advantages and disadvantages of ML methods.
MethodAdvantageDisadvantageSummary
MLRLinear relationships are estimated quickly.It cannot model nonlinear complex relationships.Speed and simplicity
RFIt enhances accuracy in complex and nonlinear concrete compositions.It requires high computational power and time.Complex and nonlinear relationships
SVRIt reduces overfitting in nonlinear datasets.Low interpretability makes component impacts hard to assess.Complex and nonlinear relationships
DTThe impact of parameters is determined through simple rules.Overfitting risks arise when too many branches are created.Visualization and interpretability
GBRIt provides high accuracy in complex datasets.Large datasets cause high processing and computational costs.High accuracy and optimized predictions
ANNIt accurately determines the impact of complex parameters.In large datasets, there is a risk of overfitting and increased costs.Complex and nonlinear relationships
ANFISThe effects of complex parameters are analyzed using fuzzy logic rules.Component effects are harder to interpret in large datasets.Hybrid solutions and flexibility
GEPIt enables accurate predictions by modeling complex concrete parameters.It requires the adjustment of parameters.High accuracy and optimized predictions
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Altuncı, Y.T. A Comprehensive Study on the Estimation of Concrete Compressive Strength Using Machine Learning Models. Buildings 2024, 14, 3851. https://doi.org/10.3390/buildings14123851

AMA Style

Altuncı YT. A Comprehensive Study on the Estimation of Concrete Compressive Strength Using Machine Learning Models. Buildings. 2024; 14(12):3851. https://doi.org/10.3390/buildings14123851

Chicago/Turabian Style

Altuncı, Yusuf Tahir. 2024. "A Comprehensive Study on the Estimation of Concrete Compressive Strength Using Machine Learning Models" Buildings 14, no. 12: 3851. https://doi.org/10.3390/buildings14123851

APA Style

Altuncı, Y. T. (2024). A Comprehensive Study on the Estimation of Concrete Compressive Strength Using Machine Learning Models. Buildings, 14(12), 3851. https://doi.org/10.3390/buildings14123851

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