Prediction Of Compressive Strength Research Articles

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing geopolymer concrete with a higher compressive strength challenging. In the performance prediction of geopolymer concrete compressive strength, machine learning models have the advantage of being more accurate and faster. However, only a single machine learning model is usually used at present, there are few applications of ensemble learning models, and model optimization processes is lacking. Therefore, this paper proposes to use the Firefly Algorithm (AF) as an optimization tool to perform hyperparameter tuning on Logistic Regression (LR), Multiple Logistic Regression (MLR), decision tree (DT), and Random Forest (RF) models. At the same time, the reliability and efficiency of four integrated learning models were analyzed. The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing ability. According to the experimental data, the RF-AF model had the lowest RMSE value. The RMSE value of the training set and test set were 4.0364 and 8.7202, respectively. The R value of the training set and test set were 0.9774 and 0.8915, respectively. Therefore, compared with the other three models, RF-AF has a stronger generalization ability and higher prediction accuracy. In addition, the molar concentration of NaOH was the most important influencing factors, and its influence was far greater than the other possible factors including NaOH content. Therefore, it is necessary to pay more attention to NaOH molarity when designing geopolymer concrete.

Prediction of unconfined compressive strength of lime treated soils

ABSTRACT Robust models based on Artificial Neural Network and simplified linear regression were proposed to predict the Unconfined Compressive Strength (UCS) of Lime Treated Soils (LTS). In total, an experimental database using 1120 test specimens was created. Critical examination of the collected experimental data suggested that there are eight key parameters that govern the attained strength gain. These input parameters are liquid limit, plastic limit, dry unit weight, water content, fine content, lime content, curing temperature and curing time whereas the only output dependent parameter is the UCS. The parameters of the proposed model including weights, biases and transfer functions were successfully converted to an explicit mathematical model relating the UCS with the key input parameters. Based on the results of the statistical evaluation, it was shown that a three-layered Artificial-Neural-Network model with 19 hidden neurons was capable to predict the UCS of (LTS) with a high degree of accuracy that was better than that achieved by the developed regression model. A coupling effect of the input parameters and weights analysis were conducted for the developed Artificial-Neural-Network-model to assess the importance of the key parameters. Analysis of weights of the model showed that curing time was the most significant factor affecting strength gain.

Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm

Landscape geopolymer concrete (GePoCo) with environmentally friendly production methods not only has a stable structure but can also effectively reduce environmental damage. Nevertheless, GePoCo poses challenges with its intricate cementitious matrix and a vague mix design, where the components and their relative amounts can influence the compressive strength. In response to these challenges, the application of accurate and applicable soft computing techniques becomes imperative for predicting the strength of such a composite cementitious matrix. This research aimed to predict the compressive strength of GePoCo using waste resources through a novel ensemble ML algorithm. The dataset comprised 156 statistical samples, and 15 variables were selected for prediction. The model employed a combination of the RF, GWO algorithm, and XGBoost. A stacking strategy was implemented by developing multiple RF models with different hyperparameters, combining their outcome predictions into a new dataset, and subsequently developing the XGBoost model, termed the RF–XGBoost model. To enhance accuracy and reduce errors, the GWO algorithm optimized the hyperparameters of the RF–XGBoost model, resulting in the RF–GWO–XGBoost model. This proposed model was compared with stand-alone RF and XGBoost models, and a hybrid GWO–XGBoost system. The results demonstrated significant performance improvement using the proposed strategies, particularly with the assistance of the GWO algorithm. The RF–GWO–XGBoost model exhibited better performance and effectiveness, with an RMSE of 1.712 and 3.485, and R2 of 0.983 and 0.981. In contrast, stand-alone models (RF and XGBoost) and the hybrid model of GWO–XGBoost demonstrated lower performance.

On the use of machine learning and data-transformation methods to predict hydration kinetics and strength of alkali-activated mine tailings-based binders

The escalating production of mine tailings (MT), a byproduct of the mining industry, constitutes significant environmental and health hazards, thereby requiring a cost-effective and sustainable solution for its disposal or reuse. This study proposes the use of MT as the primary ingredient (≥70%mass) in binders for construction applications, thereby ensuring their efficient upcycling as well as drastic reduction of environmental impacts associated with the use of ordinary Portland cement (OPC). The early-age hydration kinetics and compressive strength of MT-based binders are evaluated with an emphasis on elucidating the influence of alkali activation parameters and the amount of slag or cement that are used as minor constituents. This study reveals correlations between cumulative heat release and compressive strengths at different ages; these correlations can be leveraged to estimate the compressive strength based on hydration kinetics. Furthermore, this study presents a random forest (RF) model—in conjunction with fast Fourier and direct cosine transformation techniques to overcome the limitations associated with limited volume and diversity of the database—to enable high-fidelity predictions of time-dependent hydration kinetics and compressive strength of MT-based binders in relation to mixture design. Overall, this study demonstrates a sustainable approach to upcycle mine tailings as the primary component in low-carbon construction binders; and presents both analytical and machine learning-based approaches for accurate a priori predictions of hydration kinetics and compressive strength of these binders.

Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks

This article deals with the study of predicting the confinement effect of carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength using metaheuristics-based artificial neural networks. A detailed database of 708 CFRP confined concrete cylinders is developed from previously published research with information on eight parameters, including geometrical parameters like the diameter (d) and height (h) of a cylinder, unconfined compressive strength of concrete (f_co^'), thickness (nt), the elastic modulus of CFRP (Ef), unconfined concrete strain (?_co), confined concrete strain (?_cc) and the ultimate compressive strength of confined concrete (f_cc^'). Three metaheuristic models are implemented, including particle swarm optimization (PSO), grey wolf optimizer (GWO), and bat algorithm (BA). These algorithms are trained on the data using an objective function of mean square error, and their predicted results are validated against experimental studies and finite element analysis. The study shows that the hybrid PSO model predicted the strength of CFRP-confined concrete cylinders with a maximum accuracy of 99.13% and GWO predicted the results with an accuracy of 98.17%. The high accuracy of axial compressive strength predictions demonstrated that these prediction models are a reliable solution to the empirical methods. The prediction models are especially suitable for avoiding full-scale, time-consuming experimental tests that make the process quick and economical.

Prediction of concrete compressive strength using deep neural networks based on hyperparameter optimization

Prediction of concrete compressive strength using deep neural networks based on hyperparameter optimization

Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods

The determination of mechanical properties for different building materials is a highly relevant and practical field of application for machine learning (ML) techniques within the construction sector. When working with vibrocentrifuged concrete products and structures, it is crucial to consider factors related to the impact of aggressive environments. Artificial intelligence methods can enhance the prediction of vibrocentrifuged concrete properties through the use of specialized machine learning algorithms for materials’ strength determination. The aim of this article is to establish and evaluate machine learning algorithms, specifically Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), CatBoost (CB), for the prediction of compressive strength in vibrocentrifuged concrete under diverse aggressive operational conditions. This is achieved by utilizing a comprehensive database of experimental values obtained in laboratory settings. The following metrics were used to analyze the accuracy of the constructed regression models: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root-Mean-Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The average MAPE in the range from 2% (RF, CB) to 7% (LR, SVR) allowed us to draw conclusions about the possibility of using “smart” algorithms in the development of compositions and quality control of vibrocentrifuged concrete, which ultimately entails the improvement and acceleration of the construction and building materials manufacture. The best model, CatBoost, showed MAE = 0.89, MSE = 4.37, RMSE = 2.09, MAPE = 2% and R2 = 0.94.

Characterization and economization of cementitious tile bond adhesives using machine learning technique

Cementitious Tile Adhesives (CTAs) play a key role in ensuring the elegant outlook of buildings by improving the serviceability of various tile applications, hence making them an integral part of the dry-mix mortar industry with a significant market share globally. However, CTAs require special raw material for their production, making them a less economical option for sustainable construction applications. Hence, the current research work undertook an effort to explore the potential of different type of fine materials to produce CTAs to obtain a cheap value-added product. A total of 36 mixture proportions were prepared with six different sources of fine aggregates (i.e., silica sand, quartz sand, dune sand, river sand 1, river sand 2, and river sand 3), with six varying binder-to-fine aggregates ratios, and their mechanical strength parameters were evaluated. Further microstructure analysis was performed for fine aggregates, and best and worst performing mixture proportions to gain a deep understanding about their mechanical performance. The results revealed that the mixture proportions utilizing silica sand outperformed all other formulations in terms of compressive strength, and tensile strength with a value of 25.28 MPa, and 1.35 MPa, respectively, while dune sand performed the best in terms of shear strength 1.92 MPa with cement content of 40 % at 28 days. Moreover, microstructural investigation also showed the better microstructural performance of silica sand as compared to that of all other sources of sand. To increase the applicability of the undertaken research project, an Artificial Intelligence (AI) based neural network model, along with predictive equations, was developed for the prediction of compressive strength (R2 = 0.9831), shear strength (R2 = 0.9808), and tensile strength (R2 = 0.9862). Hence, it can be concluded that the current research work provides a foundation for an effective product development process to produce tile bond adhesives using different type of raw materials available leading to economical and sustainable manufacturing of cementitious adhesives.

Post lightning strike residual compressive strength prediction in unidirectional carbon reinforced polymer composites

Post lightning strike residual compressive strength prediction in unidirectional carbon reinforced polymer composites

Ensemble learning based compressive strength prediction of concrete structures through real-time non-destructive testing

This study conducts an extensive comparative analysis of computational intelligence approaches aimed at predicting the compressive strength (CS) of concrete, utilizing two non-destructive testing (NDT) methods: the rebound hammer (RH) and the ultrasonic pulse velocity (UPV) test. In the ensemble learning approach, the six most popular algorithms (Adaboost, CatBoost, gradient boosting tree (GBT), random forest (RF), stacking, and extreme gradient boosting (XGB)) have been used to develop the prediction models of CS of concrete based on NDT. The ML models have been developed using a total of 721 samples, of which 111 were cast in the laboratory, 134 were obtained from in-situ testing, and the other samples were gathered from the literature. Among the three categories of analytical models—RH models, UPV models, and combined RH and UPV models; seven, ten, and thirteen models have been used respectively. AdaBoost, CatBoost, GBT, RF, Stacking, and XGB models have been used to improve the accuracy and dependability of the analytical models. The RH-M5, UPV-M6, and C-M6 (combined UPV and RH model) models were found with highest performance level amongst all the analytical models. The MAPE value of XGB was observed to be 84.37%, 83.24%, 77.33%, 59.46%, and 81.08% lower than AdaBoost, CatBoost, GBT, RF, and stacking, respectively. The performance of XGB model has been found best than other soft computing techniques and existing traditional predictive models.

Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocity

ABSTRACT The current study aimed to investigate the possibility of predicting the compressive strength of geopolymer mortar by mix design parameters, ultrasonic pulse velocity (UPV) and machine learning techniques. Here the geopolymer mortar is produced from eggshell ash and rice husk ash as precursors, NaOH solution as activator and quarry waste as fine aggregate. Twenty-seven combinations of geopolymer mix and a total of 189 mortar cubes were cast and tested for UPV and compressive strength. Seven different machine learning techniques were used to predict the compressive strength assessment tools: linear regression, artificial neural networks, boosted tree regression, random forest regression, K-Nearest Neighbor, support vector regression and XGboost. Among the diverse machine learning models evaluated in this study, XGboost exhibited remarkable efficacy in forecasting the compressive strength of geopolymer mortar. The investigation conducted using SHAP indicates that the concentration of UPV shows the most substantial influence on the prediction of compressive strength.

Prediction of compressive strength and tensile strain of engineered cementitious composite using machine learning

Prediction of compressive strength and tensile strain of engineered cementitious composite using machine learning

Innovative compressive strength prediction for recycled aggregate/concrete using K-nearest neighbors and meta-heuristic optimization approaches

This paper presents a groundbreaking method for predicting the compressive strength (Fc) of recycled aggregate concrete (RAC) through the application of K-nearest neighbors (KNN) analysis. The task of designing mixture proportions to achieve the desired Fc can be remarkably intricate, owing to the intricate interplay among the components involved. Machine learning (ML) algorithms have exhibited considerable promise in tackling this complexity effectively. In pursuit of enhanced prediction accuracy, this research introduces a semi-empirical approach that seamlessly integrates strategies, including optimization techniques. This study incorporates two meta-heuristic methods, the Fire Hawk optimizer (FHO) and Runge–Kutta optimization (RUK) to enhance model accuracy. The research results reveal three separate models: KNFH, KNRK, and a single KNN model, each providing valuable insights for precise Fc prediction. Remarkably, the KNFH model stands out as a top performer, boasting an impressive R2 value of 0.994 and a meager RMSE value of 1.122. These findings not only validate the accuracy and reliability of the KNFH model but also highlight its effectiveness in predicting Fc outcomes. This approach holds great promise for precise Fc forecasting in the construction industry. Integrating meta-heuristic algorithms significantly improves model accuracy, leading to more reliable forecasts with profound implications for construction projects and their outcomes. This research marks a significant advancement in predicting Fc using ML, offering valuable tools for engineers and builders.

Compressive strength prediction of ternary blended geopolymer concrete using artificial neural networks and support vector regression

Compressive strength prediction of ternary blended geopolymer concrete using artificial neural networks and support vector regression

Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach

Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach

Accurate compressive strength prediction using machine learning algorithms and optimization techniques

Numerous components' complex interrelationships and interconnectedness present a formidable obstacle in developing mix designs for high-performance concrete (HPC) formulation. The effectiveness of machine learning (ML) algorithms in resolving this paradox has been illustrated. However, they are classified as opaque black-box models due to the lack of a discernible correlation between blend ratios and compressive durability. The present study proposes a semi-empirical methodology that integrates various techniques, including non-dimensionalization and optimization, to overcome this constraint. The methodology exhibits a noteworthy level of accuracy when forecasting compressive strength (CS) across a spectrum of divergent datasets, thus evincing its extensive and all-encompassing efficacy. Moreover, the precise relationship that semi-empirical equations convey is of great significance to practitioners and researchers in this field, especially with respect to their predictive abilities. The determination of CS in concrete is a critical facet of the design of HPC. An exhaustive comprehension of the intricate interplay between manifold factors is requisite to attain an ideal blend proportion. The study’s findings indicate that RF can accurately predict CS. Moreover, the combination of optimization algorithms significantly enhances the model’s effectiveness. Among the three Optimization Algorithms under consideration, the COA optimizer has exhibited superior performance in augmenting the accuracy and precision of the RF prediction model for CS. As a result, RFCO obtained the more suitable value of R2 and RMSE obtained 0.998 and 0.88, alternatively.

Compressive Strength Prediction Model of High Strength Concrete by Destructive and Nondestructive Technique

Concrete’s compressive strength can be tested in a laboratory before construction begins. Since concrete is a natural material and cannot be destroyed, it is not possible to determine its compressive strength through destructive testing. Rebound hammers are typically used in the field to evaluate the structural elements’ ability to withstand hardened concrete. As part of the current study, a comparison was made between concrete’s compressive strength measured by destructive testing and its surface hardness measured by rebound hammering. Tests were conducted on laboratory-made concrete cubes in this study to determine destructive and non-destructive behavior. Minitab software was used for regression analysis. Schmidt rebound hammer tests, a type of nondestructive testing (NDT), were shown to have very strong relationships with concrete destructive compression tests. Schmidt rebound hammers are commonly used to measure the surface hardness of concrete, since the hammer rebound number and concrete strength are theoretically correlated. Utilising a Schmidt hammer, it was applied. Standard concrete cubes with crushing strengths between 20 and 30 MPa were created using various mix proportions. Using regression analysis, destructive and non-destructive values are correlated. The linear regression equation is well suited for obtaining the compressive strength using rebound value by using linear regression equation.

Deep Learning based Concrete Compressive Strength Prediction with Modified Resilient Backpropagation Training

Deep Learning based Concrete Compressive Strength Prediction with Modified Resilient Backpropagation Training

Compressive Strength Prediction of High-Performance Hydraulic Concrete using a Novel Neural Network Based on the Memristor

Compressive Strength Prediction of High-Performance Hydraulic Concrete using a Novel Neural Network Based on the Memristor

Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms

Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms