Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

An ensemble learning-based prediction model for the compressive strength degradation of concrete containing superabsorbent polymers (SAP)

Super absorbent polymer (SAP) has a capacity to enhance the characteristics of cementitious composites in both their fresh and hardened forms. However, it is essential to recognize that the strength of SAP concrete may decrease. By altering the concrete composition and selecting the appropriate type of SAP, it is possible to reduce this reduction. This work employs machine learning (ML) to tackle the issue of strength degradation. The analysis considers ten distinct variables linked to concrete composition and the type of SAP. The study uses machine learning approaches that involve both regression and classification tasks. The use of ensemble learning greatly improves the quality and accuracy of the results, showing its superiority in combining several models to produce more precise predictions. The findings demonstrate that the Support Vector Machines (SVM) and Extreme Gradient Boosting (XGBoost) regression algorithms accurately forecasted the percentage of reduction in strength in SAP concrete. These predictions were based on the concrete composition and SAP details, resulting in R2 values of 0.90 and 0.88, respectively. Furthermore, XGBoost exhibited the highest accuracy, reaching 0.94, when compared to the various categorization algorithms. According to the results, the mean squared error (MSE) of the ensemble model demonstrated superior outcomes. Furthermore, the SHapley Additive exPlanations (SHAP) reveal that some variables, including SAP%, SAP size, and compressive strength, have a significant influence on the strength reduction model. This study aims to bridge the gap between academic research and practical application by developing a web application that employs ensemble learning to precisely forecast the reduction in compressive strength caused by the usage of SAP.

Decision tree models for the estimation of geo-polymer concrete compressive strength

The green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring the compressive strength of geo-polymer concretes (CSGPoC) needs a significant amount of work and expenditure. Therefore, the best idea is predicting CSGPoC with a high level of accuracy. To do this, the base learner and super learner machine learning models were proposed in this study to anticipate CSGPoC. The decision tree (DT) is applied as base learner, and the random forest and extreme gradient boosting (XGBoost) techniques are used as super learner system. In this regard, a database was provided involving 259 CSGPoC data samples, of which four-fifths of is considered for the training model and one-fifth is selected for the testing models. The values of fly ash, ground-granulated blast-furnace slag (GGBS), Na2SiO3, NaOH, fine aggregate, gravel 4/10 mm, gravel 10/20 mm, water/solids ratio, and NaOH molarity were considered as input of the models to estimate CSGPoC. To evaluate the reliability and performance of the decision tree (DT), XGBoost, and random forest (RF) models, 12 performance evaluation metrics were determined. Based on the obtained results, the highest degree of accuracy is achieved by the XGBoost model with mean absolute error (MAE) of 2.073, mean absolute percentage error (MAPE) of 5.547, Nash-Sutcliffe (NS) of 0.981, correlation coefficient (R) of 0.991, R2 of 0.982, root mean square error (RMSE) of 2.458, Willmott's index (WI) of 0.795, weighted mean absolute percentage error (WMAPE) of 0.046, Bias of 2.073, square index (SI) of 0.054, p of 0.027, mean relative error (MRE) of -0.014, and a20 of 0.983 for the training model and MAE of 2.06, MAPE of 6.553, NS of 0.985, R of 0.993, R2 of 0.986, RMSE of 2.307, WI of 0.818, WMAPE of 0.05, Bias of 2.06, SI of 0.056, p of 0.028, MRE of -0.015, and a20 of 0.949 for the testing model. By importing the testing set into trained models, values of 0.8969, 0.9857, and 0.9424 for R2 were obtained for DT, XGBoost, and RF, respectively, which show the superiority of the XGBoost model in CSGPoC estimation. In conclusion, the XGBoost model is capable of more accurately predicting CSGPoC than DT and RF models.

Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis

Self-compacting mortar (SCM) has superior workability and long-term durable performance compared to traditional mortar. The strength of SCM, including both its compressive and flexural strengths, is a crucial property that is determined by appropriate curing conditions and mix design parameters. In the context of materials science, predicting the strength of SCM is challenging because of multiple influencing factors. This study employed machine learning techniques to establish SCM strength prediction models. Based on ten different input parameters, the strength of SCM specimens were predicted using two different types of hybrid machine learning (HML) models, namely Extreme Gradient Boosting (XGBoost) and the Random Forest (RF) algorithm. HML models were trained and tested by experimental data from 320 test specimens. In addition, the Bayesian optimization method was utilized to fine tune the hyperparameters of the employed algorithms, and cross-validation was employed to partition the database into multiple folds for a more thorough exploration of the hyperparameter space while providing a more accurate assessment of the model's predictive power. The results show that both HML models can successfully predict the SCM strength values with high accuracy, and the Bo-XGB model demonstrated higher accuracy (R² = 0.96 for training and R² = 0.91 for testing phases) for predicting flexural strength with low error. In terms of compressive strength prediction, the employed BO-RF model performed very well, with R² = 0.96 for train and R² = 0.88 testing stages with minor errors. Moreover, the SHAP algorithm, permutation importance and leave-one-out importance score were used for sensitivity analysis to explain the prediction process and interpret the governing input variable parameters of the proposed HML models. Finally, the outcomes of this study might be applied to guide the future mix design of SCM specimens.

Prediction of the shear capacity of ultrahigh-performance concrete beams using neural network and genetic algorithm

Currently, concrete structures have increasingly higher requirements for the shear capacity of beams, and ultrahigh-performance concrete (UHPC) beams are increasingly widely used. To facilitate the design of UHPC beams, this paper constructs a UHPC beam shear strength prediction model. First, static shear tests were conducted on 6 UHPC beam specimens with a length of 2 m and a cross-sectional size of 200 mm × 300 mm to explore the effects of the UHPC strength, shear span ratio, hoop ratio, and steel fiber content on the shear resistance and failure morphology of the UHPC beams. Based on the results of this study and a static load experiment of 102 UHPC beams in the literature, the construction includes the shear span ratio (λ), beam section width (b), beam section height (h), hoop ratio (ρ_SV), UHPC compressive strength (f_c), steel fiber volume fraction (V_f), and the UHPC beam shear capacity (V_ex) 7 parameter database. Based on the construction of the database, 1200 BPNN models were trained through trial and error. The models were evaluated using the correlation coefficient R, root mean square error RMSE, and a20-index indicators, and the optimal BPNN model (6-15-8-1) was determined based on the ranking of RMSE. After the optimal BPNN is optimized by a genetic algorithm, the prediction performance of the model is improved. The correlation coefficient between the predicted value and the experimental value is R² = 0.98667, and RMSE = 7.38. This model can reliably predict the shear strength of UHPC beams and provide designers with a reference for the design of UHPC beams. Finally, after sensitivity analysis, the influence of each input parameter on the UHPC shear capacity is determined.

Mechanical Properties of Ultra-High Performance Concrete with Coal Gasification Coarse Slag as River Sand Replacement

Coal gasification coarse slag (CGCS) is a by-product of coal gasification. Despite its abundance, CGCS is mostly used in boiler blending, stacking, and landfill. Large-scale industrial applications of CGCS can be environment-friendly and cost saving. In this study, the application of CGCS as a substitute for river sand (RS) with different replacement ratios in ultra-high performance concrete (UHPC) was investigated. The effects of CGCS replacement ratios on the fluidity and mechanical properties of specimens were examined, and the effect mechanisms were explored on the basis of hydration products and the multi-scale (millimetre-scale and micrometre-scale) microstructure analysis obtained through X-ray diffraction (XRD), scanning electron microscopy, and X-ray energy-dispersive spectroscopy. With an increase in the CGCS replacement ratio, the water-binder ratio (w/b), flexural strength, and compressive strength decreased. Specimens containing CGCS of ≤25% can satisfy the strength requirement of non-structural UHPC, with flexure strength of 29 MPa and compressive strength of 111 MPa at day 28. According to the XRD results and multi-scale microstructure analysis, amorphous glass beads in CGCS positively influenced ettringite generation due to the pozzolanic activity. Porous carbon particles in CGCS showed strong interfacial bonding with cement slurry due to internal hydration; this bonding was conducive to improving the mechanical strength. However, CGCS hindered hydration in the later curing stage, leading to an increase in the unreacted cement and agglomeration of fly ash; in addition, at a CGCS replacement ratio of up to 50%, an apparent interfacial transition zone structure was observed, which was the main contributor to mechanical strength deterioration.

Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods

This research employed machine learning (ML) and SHapley Additive ExPlanations (SHAP) methods to assess the strength and impact of raw ingredients of cement mortar (CM) incorporated with waste glass powder (WGP). The data required for this study were generated using an experimental approach. Two ML methods were employed, i.e., gradient boosting and random forest, for compressive strength (CS) and flexural strength (FS) estimation. The performance of ML approaches was evaluated by comparing the coefficient of determination (R²), statistical checks, k-fold assessment, and analyzing the variation between experimental and estimated strength. The results of the ML-based modeling approaches revealed that the gradient boosting model had a good degree of precision, but the random forest model predicted the strength of the WGP-based CM with a greater degree of precision for CS and FS prediction. The SHAP analysis revealed that fine aggregate was a critical raw material, with a stronger negative link to the strength of the material, whereas WGP and cement had a greater positive effect on the strength of CM. Utilizing such approaches will benefit the building sector by supporting the progress of rapid and inexpensive approaches for identifying material attributes and the impact of raw ingredients.

Artificial Intelligence Models for the Mass Loss of Copper-Based Alloys under Cavitation

Cavitation is a physical process that produces different negative effects on the components working in conditions where it acts. One is the materials' mass loss by corrosion-erosion when it is introduced into fluids under cavitation. This research aims at modeling the mass variation of three samples (copper, brass, and bronze) in a cavitation field produced by ultrasound in water, using four artificial intelligence methods-SVR, GRNN, GEP, and RBF networks. Utilizing six goodness-of-fit indicators (R2, MAE, RMSE, MAPE, CV, correlation between the recorded and computed values), it is shown that the best results are provided by GRNN, followed by SVR. The novelty of the approach resides in the experimental data collection and analysis.

New SHapley Additive ExPlanations (SHAP) Approach to Evaluate the Raw Materials Interactions of Steel-Fiber-Reinforced Concrete

Recently, artificial intelligence (AI) approaches have gained the attention of researchers in the civil engineering field for estimating the mechanical characteristics of concrete to save the effort, time, and cost of researchers. Consequently, the current research focuses on assessing steel-fiber-reinforced concrete (SFRC) in terms of flexural strength (FS) prediction by employing delicate AI techniques as well as to predict the raw material interaction that is still a research gap. In this study, the FS of SFRC is estimated by deploying supervised machine learning (ML) techniques, such as DT-Gradient Boosting, DT-XG Boost, DT-AdaBoost, and DT-Bagging. In addition to that, the performance model is also evaluated by using R², root mean square error (RMSE), and mean absolute error (MAE). Furthermore, the k-fold cross-validation method is also applied to validate the model's performance. It is observed that DT-Bagging with an R² value of 0.95 is superior to DT-XG Boost, DT-Gradient Boosting, and DT-AdaBoost. Lesser error MAE and RMSE and higher R² values for the DT-Bagging model show the enhanced performance of the model compared to the other ensembled approaches. Considerable conservation of time, effort, and cost can be made by applying ML techniques to predict concrete properties. The evaluation of the outcome depicts that the estimated results of DT-Bagging are closer to the experimental results, indicating the accurate estimation of SFRC flexural strength. It is further revealed from the SHapley Additive exPlanations (SHAP) study that the volumetric content of steel fiber highly and positively influences the FS of SFRC.

Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms

Steel-fiber-reinforced concrete (SFRC) has been introduced as an effective alternative to conventional concrete in the construction sector. The incorporation of steel fibers into concrete provides a bridging mechanism to arrest cracks, improve the post-cracking behavior of concrete, and transfer stresses in concrete. Artificial intelligence (AI) approaches are in use nowadays to predict concrete properties to conserve time and money in the construction industry. Accordingly, this study aims to apply advanced and sophisticated machine-learning (ML) algorithms to predict SFRC compressive strength. In the current work, the applied ML approaches were gradient boosting, random forest, and XGBoost. The considered input variables were cement, fine aggregates (sand), coarse aggregates, water, silica fume, super-plasticizer, fly ash, steel fiber, fiber diameter, and fiber length. Previous studies have not addressed the effects of raw materials on compressive strength in considerable detail, leaving a research gap. The integration of a SHAP analysis with ML algorithms was also performed in this paper, addressing a current research need. A SHAP analysis is intended to provide an in-depth understanding of the SFRC mix design in terms of its strength factors via complicated, nonlinear behavior and the description of input factor contributions by assigning a weighing factor to each input component. The performances of all the algorithms were evaluated by applying statistical checks such as the determination coefficient (R²), the root mean square error (RMSE), and the mean absolute error (MAE). The random forest ML approach had a higher, i.e., 0.96, R² value with fewer errors, producing higher precision than other models with lesser R² values. The SFRC compressive strength could be anticipated by applying the random forest ML approach. Further, it was revealed from the SHapley Additive exPlanations (SHAP) analysis that cement content had the highest positive influence on the compressive strength of SFRC. In this way, the current study is beneficial for researchers to effectively and quickly evaluate SFRC compressive strength.

Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions

Incorporating waste material, such as recycled coarse aggregate concrete (RCAC), into construction material can reduce environmental pollution. It is also well-known that the inferior properties of recycled aggregates (RAs), when incorporated into concrete, can impact its mechanical properties, and it is necessary to evaluate the optimal performance. Accordingly, artificial intelligence has been used recently to evaluate the performance of concrete compressive behaviour for different types of construction material. Therefore, supervised machine learning techniques, i.e., DT-XG Boost, DT-Gradient Boosting, SVM-Bagging, and SVM-Adaboost, are executed in the current study to predict RCAC’s compressive strength. Additionally, SHapley Additive exPlanations (SHAP) analysis shows the influence of input parameters on the compressive strength of RCAC and the interactions between them. The correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) are used to assess the model’s performance. Subsequently, the k-fold cross-validation method is executed to validate the model’s performance. The R2 value of 0.98 from DT-Gradient Boosting supersedes those of the other methods, i.e., DT- XG Boost, SVM-Bagging, and SVM-Adaboost. The DT-Gradient Boosting model, with a higher R2 value and lower error (i.e., MAE, RMSE) values, had a better performance than the other ensemble techniques. The application of machine learning techniques for the prediction of concrete properties would consume fewer resources and take less time and effort for scholars in the respective engineering field. The forecasting of the proposed DT-Gradient Boosting models is in close agreement with the actual experimental results, as indicated by the assessment output showing the improved estimation of RCAC’s compressive strength.

Editorial for “Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models”

Concrete is one of the most widespread materials in the civil engineering field due to its versatility for both structural and non-structural applications depending on the density range, competitiveness in terms of durability and manufacturing costs, as well as ease in finding raw constituent elements [...]

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

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.

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

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.