Prediction Of Compressive Strength Research Articles

Strength prediction of modified self-compacting concrete using response surface method

AbstractThis study highlights the effectiveness of the response surface method in predicting the properties of hardened concrete containing spent foundry sand and manufactured-sand. In current research, different mix proportions are prepared with spent foundry sand and manufactured-sand as partial replacements for fine aggregate. The workability, strength, and durability studies are conducted on all concrete mixes. The test results confirm that the inclusion of 20% of spent foundry sand is optimum for enhancing the strength and durability. Also, the inclusion of 20% of spent foundry sand in manufactured-sand concrete also possesses optimum results in strength and durability. Further, the response surface method has been employed to develop a model for compressive strength prediction. The R-squared and residual sum of squares of error for the compressive strength model were 0.9863 and 2.345, which confirms the goodness of the fit.

Statistical Reliability Analysis of Ultrasonic Velocity Method for Predicting Residual Strength of High-Strength Concrete under High-Temperature Conditions.

Herein, we conducted a comprehensive statistical assessment of the ultrasonic pulse velocity (UPV) method's effectiveness in predicting concrete strength under diverse conditions, specifically early age, middle age, and high-temperature exposure. The concrete mixtures, with water-to-cement (W/C) ratios of 0.33 and 0.28, were classified as granite aggregate or coal-ash aggregate mixes. Compressive strength and UPV measurements were performed under these conditions, and subsequent statistical analyses treated the identified factors as distinct groups. The results revealed a substantial difference in compressive strength between specimens at early age (average of 13.01) and those at middle age (average of 41.96) and after high-temperature exposure (average of 48.08). Conversely, UPV analysis showed an insignificant difference between the early-age specimens and those after high-temperature exposure. The analysis of the W/C ratio and coarse aggregate demonstrated significant differences (p-value < 0.05) in compressive strength between specimens in middle age and those exposed to high temperatures, excluding the early-age specimens. However, UPV analysis revealed insignificant differences, with p-values of 0.67 and 0.38 between specimens at an early age and post-high-temperature exposure, respectively. Regression analysis identified suitable functions for each scenario, emphasizing the importance of a strength prediction model for concrete after high-temperature exposure, particularly considering the W/C ratio. Since concrete showed statistically different compressive strength, UPV, and strength prediction models in three conditions (early age, middle age, and high temperature), different strength prediction models must be used for the purpose of accurately predicting the strength of concrete.

Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm

There are many factors that affect the compressive strength of concrete. The relationship between compressive strength and these factors is a complex nonlinear problem. Empirical formulas commonly used to predict the compressive strength of concrete are based on summarizing experimental data of several different mix proportions and curing periods, and their generality is poor. This article proposes an improved artificial bee colony algorithm (IABC) and a multilayer perceptron (MLP) coupled model for predicting the compressive strength of concrete. To address the shortcomings of the basic artificial bee colony algorithm, such as easily falling into local optima and slow convergence speed, this article introduces a Gaussian mutation operator into the basic artificial bee colony algorithm to optimize the initial honey source position and designs an MLP neural network model based on the improved artificial bee colony algorithm (IABC-MLP). Compared with traditional strength prediction models, the ABC-MLP model can better capture the nonlinear relationship of the compressive strength of concrete and achieve higher prediction accuracy when considering the compound effect of multiple factors. The IABC-MLP model built in this study is compared with the ABC-MLP and particle swarm optimization (PSO) coupling algorithms. The research shows that IABC can significantly improve the training and prediction accuracy of MLP. Compared with the ABC-MLP and PSO-MLP coupling models, the training accuracy of the IABC-MLP model is increased by 1.6% and 4.5%, respectively. This model is also compared with common individual learning algorithms such as MLP, decision tree (DT), support vector machine regression (SVR), and random forest algorithms (RF). Based on the comparison of prediction results, the proposed method shows excellent performance in all indicators and demonstrates the superiority of heuristic algorithms in predicting the compressive strength of concrete.

Study on Coordinated Deformation Failure Mechanism and Strength Prediction Model of Rock-lining Concrete

Shotcrete has been widely used in the excavation of caverns and tunnels as a key load-bearing element in the support system. However, the interfacial stresses and bond strengths within the composite structures formed by the interaction of geologic bodies and tectonic formations are not well understood, which can lead to safety accidents and excessive wastage of support materials in engineering. To address this gap, rock-concrete composite specimens were created using six distinct types of surrounding rocks. The strength law of composite specimens was analyzed, and the uniaxial compressive strength prediction model of composite specimens was derived based on Mohr−Coulomb strength yield criterion. The results show that the uniaxial compressive strength of the composite specimen changes with the change of rock type, which is between the strength of rock and concrete. The uniaxial peak strength and elastic modulus of composite specimens are linearly positively correlated with the ratio (Y) of concrete-rock elastic modulus, which is easily affected by concrete properties. However, the peak strain is linearly and negatively correlated with Y, which is greatly influenced by rock. The rock-concrete interface of the composite specimen plays an important role in the failure of the specimen, which changes the stress transfer state of the composite specimen, produces uncoordinated deformation, and finally causes the failure of the specimen. The theoretical and test value error of the derived composite specimen uniaxial compressive strength prediction model are –1.19%~12.20%, and the theoretical calculation model can be used to predict the uniaxial compressive strength of rock-concrete composite specimens.

Experimental investigation of cold-formed stainless steel built-up closed section stub columns

This paper presents an experimental investigation on the performance of cold-formed stainless steel built-up closed section stub columns. The built-up sections were composed by assembling two channel components at the flanges, which were brake-pressed from high-strength austenitic and duplex stainless steel thin sheets. Three types of built-up sectional profiles were formed by connecting two unstiffened channels (i.e., unlipped channels), two edge-stiffened channels (i.e., lipped channels) as well as one unstiffened channel and one edge-stiffened channel, respectively, using self-plugging rivets. Four sectional series were devised to cover nominal overall web height-to-plate thickness ratio values of 50, 60, 67 and 80. In order to obtain the actual material properties considering the cold-work effect, tensile coupon tests were carried out on both flat and corner longitudinal coupon specimens. In addition, a total of 29 fixed-ended stub columns including 19 specimens without holes and 10 specimens with circular web holes were tested under axial compression. The experimental results involving failure modes, ultimate capacities and responses of load versus axial shortening were obtained and fully documented. The effects of sectional profile types, overall web height-to-plate thickness ratio and hole diameter-to-overall web height ratio on the performance of cold-formed stainless steel built-up closed section stub columns were examined. Moreover, the appropriateness of design rules as prescribed in the recently updated American Specification ASCE/SEI 8–22 was evaluated. It was revealed that the codified design provisions generally provided unconservative strength predictions for the high-strength austenitic and duplex stainless steel built-up closed section stub columns both without and with circular web holes. Reliability analyses were performed in this study, and the compressive strength predictions of the built-up closed sections according to the existing design rules were found to be unreliable for the high-strength austenitic stainless steel stub columns without holes as well as the duplex stainless steel stub columns without and with circular web holes.

Numerical Prediction of Compressive Residual Strengths of a Quasi-Isotropic Laminate with Low-Velocity Impact Delamination

Numerical Prediction of Compressive Residual Strengths of a Quasi-Isotropic Laminate with Low-Velocity Impact Delamination

Optimized kernel extreme learning machine using Sine Cosine Algorithm for prediction of unconfined compression strength of MICP cemented soil.

Microbially induced calcite precipitation (MICP) is an eco-friendly bio-remediation technology. The unconfined compressive strength (UCS) of MICP cemented soil is an important indicator of repair effectiveness. This study proposes a machine learning technique utilizing the Sine Cosine Algorithm (SCA) to optimize the regularization coefficient C and kernel width γ of the kernel extreme learning machine (KELM) to predict the UCS of MICP cemented soil. To evaluate the performance of the proposed models, a dataset containing 180 groups of the UCS of MICP cemented soil was obtained. The results obtained by SCA-KELM were compared with those obtained by the Random Forest algorithm (RF), Support Vector Machine (SVM), and KELM. The performance of these models was evaluated by the scores of MAE, RMSE, and R2. The results indicate that the SCA-KELM algorithm exhibits optimal prediction performance (Total score: 21). After optimizing KELM with SCA, the total score improved by 110%, suggesting that SCA significantly enhances the KELM performance. After model development, the optimal population size for SCA-KELM was determined to be 50. Based on the mutual information test, an innovative method was developed for categorizing factor sensitivity by employing importance scores as the partitioning criterion. This method categorizes the influencing factors into three tiers: high (importance score: 8.03-11.14%), medium (importance score: 5.93-7.25%), and low (importance score: 3.23-5.18%). These results suggest that the proposed SCA-KELM algorithm can be regarded as a powerful tool for predicting the UCS of MICP cemented soil.

Using radial basis function for estimating the high-performance concrete compressive strength with optimizing AVOA and SSA

Compressive strength (CS) is concrete’s most important mechanical property, as it plays an important role in setting design criteria. Thus, an accurate and early assessment of the CS of concrete can minimize time, labor, and cost. This paper investigated the ability of the Radial Basis Function (RBF) to handle the prediction of CS. The nonlinearities raised from the novel utilized admixtures between the input variables and output CS is tried to be conducted with the RBF model. In order to make a flexible framework combination of the RBF model with the African Vulture Optimization (AVOA) and Salp Swarm Algorithm (SSA) techniques are considered. The results achieved from the RBF-AVOA model indicated good agreement between the actual and predicted values. The proposed model provides a very accurate HPC compressive strength prediction. In addition, the correlation coefficient R2 is equal to (0.997), and the values of mean absolute error (MAE) (0.1917 MPa), root mean square error (RMSE) (0.937 MPa), and variance account coefficient (VAF) (99.73%) are low. The performance of the RBF-AVOA model, compared to other models, provided the desired advantage and more stable predictions. AVOA plays a key role in modeling results, improving generalization capabilities, avoiding redundant data, and decreasing uncertainty.

Macroscopic compressive strength study of historical grey bricks based on microscopic scale

Determining the macroscopic compressive strength of original grey bricks in historical structures has become a crucial topic due to rules and legislation about the preservation of cultural artifacts and illnesses like weathering. This work aims to provide a microscale approach for estimating the macroscopic compressive strength of historical grey bricks. This method, based on nanoindentation and mercury intrusion porosimetry, divides the historical grey bricks into two representative volume elements (RVEs) at the observation scale with continuous transition: A microscale Mori-Tanaka equivalent RVE encompassing microstructural features, which nanoindentation, X-ray diffraction, scanning electron microscopy, mercury intrusion porosimetry, and other tests obtain the features. A mesoscale RVE representing the two-phase model of a "porous inclusions-equivalent matrix." Then, the prediction of macroscopic compressive strength of the historical grey brick is realized by the simulation of the uniaxial compression of the mesoscale RVE. For the three types of historical grey bricks (A, B, and C), the errors in the predictions obtained by the multiscale micromechanical models, in comparison to the macroscopic experimental results, are 13.67%, 10.34%, and 5.22%, respectively. Compared to conventional mechanical experiments and non-destructive methods, this approach exhibits comprehensive information acquisition, minimal influence from human factors, and minimal subject disturbance. It is well-suited for obtaining performance parameters of intrinsic materials in historical buildings, grottoes, nuclear power plants, etc.

Data-driven PSO-CatBoost machine learning model to predict the compressive strength of CFRP- confined circular concrete specimens

This work articulates the development of a sophisticated machine-learning model for the prediction of compressive strength in Carbon Fiber-Reinforced Polymer Confined-Concrete (CFRP-CC) specimens. Despite extensive empirical studies conducted over the last three decades, prevailing predictive models predominantly rooted in linear or nonlinear regression analyses are constrained by their dependency on limited data scopes. Addressing this deficiency, our research delineates the formulation of an innovative Particle Swarm Optimization- Categorical Boosting (PSO-CatBoost) algorithm, underpinned by an expansive database encompassing 916 experimental outcomes from 105 scholarly articles, spanning the period from 1991 to mid-2023. This innovative approach effectively combines the strengths of Particle Swarm Optimization and the CatBoost algorithm. It carefully evaluates various vital factors that affect the compressive strength of CFRP-CC. The uniqueness of our approach is further accentuated through the application of SHapley Additive exPlanations (SHAP) and Permutation Feature Importance (PFI) methodologies, thereby elucidating the relative importance of each contributory feature. In an unprecedented comparative analysis, the PSO-CatBoost model is rigorously benchmarked against six contemporary machine learning paradigms: CatBoost, XgBoost, AdaBoost, GBoost, Extra Trees, and Random Forest. Furthermore, this model is assessed against six empirical models for further comparison. The model exhibits superior predictive efficacy, evidenced by an exemplary coefficient of determination R-squared of 0.9847, surpassing the methodologies. This research introduces a new predictive model for CFRP-CC and represents a significant shift in concrete research, moving towards a more sophisticated, data-driven, and machine learning-focused methodology. This work thus establishes a new benchmark in the predictive modeling realm for CFRP-CC compressive strength, offering a robust and comprehensive analytical tool for both researchers and practitioners in the field. Lastly, a graphical user interface was designed for modeling the compressive strength of CFRP-CC to facilitate practical use.

FRCM Confinement of Masonry: Strain Model Assessment and New Proposals.

One of the main limitations to the use of fabric-reinforced cementitious matrix (FRCM) composites for the external confinement of masonry is the lack of accurate formulas for estimating the compressive strength and ultimate strain of confined members. With the aim of providing a contribution on the topic, the authors have been carrying out studies on the FRCM-confined masonry for some time and, in a recent study, they proposed some formulations for the prediction of compressive strength. In continuity to that work, an analytical study on the ultimate strain of FRCM-confined masonry is presented in this paper, and preliminary models were derived by considering a wide experimental database compiled from the technical literature. The accuracy of the found relationships was examined based on a comparison with the few formulas published in the literature or reported in international guidelines. To this purpose, it is worth highlighting that the current Italian Guidelines CNR-DT 215/2018 do not provide indications about the estimation of the ultimate strain of FRCM-confined masonry, and the study proposed here attempts to provide a contribution to the mentioned document.

Prediction of the Unconfined Compressive Strength of Salinized Frozen Soil Based on Machine Learning

Unconfined compressive strength (UCS) is an important parameter of rock and soil mechanical behavior in foundation engineering design and construction. In this study, salinized frozen soil is selected as the research object, and soil GDS tests, ultrasonic tests, and scanning electron microscopy (SEM) tests are conducted. Based on the classification method of the model parameters, 2 macroscopic parameters, 38 mesoscopic parameters, and 19 microscopic parameters are selected. A machine learning model is used to predict the strength of soil considering the three-level characteristic parameters. Four accuracy evaluation indicators are used to evaluate six machine learning models. The results show that the radial basis function (RBF) has the best UCS predictive performance for both the training and testing stages. In terms of acceptable accuracy and stability loss, through the analysis of the gray correlation and rough set of the three-level parameters, the total amount and proportion of parameters are optimized so that there are 2, 16, and 16 macro, meso, and micro parameters in a sequence, respectively. In the simulation of the aforementioned six machine learning models with the optimized parameters, the RBF still performs optimally. In addition, after parameter optimization, the sensitivity proportion of the third-level parameters is more reasonable. The RBF model with optimized parameters proved to be a more effective method for predicting soil UCS. This study improves the prediction ability of the UCS by classifying and optimizing the model parameters and provides a useful reference for future research on salty soil strength parameters in seasonally frozen regions.

Analysis and prediction of compressive and split-tensile strength of secondary steel fiber reinforced concrete based on RBF fuzzy neural network model.

Accurate analysis of the strength of steel-fiber-reinforced concrete (SFRC) is important for ensuring construction quality and safety. Cube compression and splitting tensile tests of steel fiber with different varieties, lengths, and dosages were performed, and the effects of different varieties, lengths, and dosages on the compressive and splitting properties of secondary concrete were obtained. It was determined that the compression and splitting strengths of concrete could be effectively improved by the addition of end-hooked and milled steel fibers. The compressive and splitting strengths of concrete can be enhanced by increasing the fiber length and content. However, concrete also exhibits obvious uncertainty owing to the comprehensive influence of steel fiber variety, fiber length, and fiber content. In order to solve this engineering uncertainty, the traditional RBF neural network is improved by using central value and weight learning strategy especially. On this basis, the RBF fuzzy neural network prediction model of the strength of secondary steel fiber-reinforced concrete was innovatively established with the type, length and content of steel fiber as input information and the compressive strength and splitting tensile strength as output information. In order to further verify the engineering reliability of the prediction model, the compressive strength and splitting tensile strength of steel fiber reinforced concrete with rock anchor beams are predicted by the prediction model. The results show that the convergence rate of the prediction model is increased by 15%, and the error between the predicted value and the measured value is less than 10%, which is more efficient and accurate than the traditional one. Additionally, the improved model algorithm is efficient and reasonable, providing technical support for the safe construction of large-volume steel fiber concrete projects, such as rock anchor beams. The fuzzy random method can also be applied to similar engineering fields.

Explainable Prediction of Compressive Strength and Elastic Modulus for Concrete Containing Waste Foundry Sand Using Bayesian-Optimized XGBoost with 10-Fold Cross-Validation

Explainable Prediction of Compressive Strength and Elastic Modulus for Concrete Containing Waste Foundry Sand Using Bayesian-Optimized XGBoost with 10-Fold Cross-Validation

Prediction of Compressive Strength of Silica Fume Blended High Strength Concrete Using Response Surface Methodology Approach

Objectives: In this study, a model was developed to predict the compressive strength of High Strength Concrete (HSC) mixed with silica fume using Response Surface Methodology (RSM). This study investigated the effects of cement, water, Silica Fume (SF), Coarse Aggregate (CA), and silica fume-cement ratio (SF/C) on the 28-day compressive strength of HSC. Silica fume is added with varying amounts of SF (5% to 25%) to cement content. Methods: Response surface methodology (RSM) was performed to investigate the influence of independent variables on the compressive strength of HSC. Findings: Analysis of the response surface plot reveals a remarkably low error percentage of less than 5%. This reveals a high degree of confidence (95%) in the model's accuracy. This study yielded a coefficient of determination (R2) of 0. 9968. It is observed negligible deviation between predicted and actual 28-day compressive strength values, indicating high model accuracy. Novelty: The predicted equation is reasonably predicting the compressive strength of high strength concrete. Keywords: High strength concrete, Response surface methodology, Silica fume, Compressive strength, Prediction model

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.