Prediction Of Compressive Strength Research Articles

Prediction of the Unconfined Compressive Strength of a One-Part Geopolymer-Stabilized Soil Using Deep Learning Methods with Combined Real and Synthetic Data

Prediction of the Unconfined Compressive Strength of a One-Part Geopolymer-Stabilized Soil Using Deep Learning Methods with Combined Real and Synthetic Data

Deep learning neural networks for monitoring early-age concrete strength through a surface-bonded PZT sensor configuration

Monitoring the immediate evolution of concrete’s strength is essential to ensure structural integrity and construction efficiency, requiring Forecasting to avoid unforeseen and severe failures during construction. The present investigation introduces an Equivalent structural parametric (ESP) study using a surface-bonded piezo sensor and electromechanical impedance (EMI) methodology to monitor and forecast the strength of concrete by implementing machine learning and deep learning methods. The concrete hydration processes are simulated using COMSOL™ 5.5. The concrete cube’s hydration is represented by altering Young’s modulus and damping ratio to show the rate of curing. Concrete strength development is examined in terms of conductance resonant frequency (CRF) and Conductance resonant peak (CRP). Continuous conductance signature monitoring and data analysis show that CRF and CRP increase with compressive strength. The system’s mechanical impedance is measured, and EMI signatures vs. frequency plots within a specific frequency range are compared to healthy impedance graphs. A mass-spring-damper system with identical properties is identified, and relevant structural parameters are computed. Modern ML algorithms include linear regression (LR), interaction LR, fine, medium, and coarse Gaussian SVM, etc. reliably predict strength with an error rate of less than 2%. Convolutional neural networks (CNN) have advanced image-based recognition, but their usage in EMI-based structural strength assessment is still being studied. A unique strategy using 2D CNN, 2D CNN– Long short-term memory (LSTM), and 2D CNN Bidirectional-LSTM to forecast concrete structure compressive strength shows the potential of deep learning. The proposed 2D CNN-Bi-LSTM model excels in compressive strength prediction, obtaining an R2 value of 0.99 and stabilizing loss error over a few epochs.

A Prediction Model for the Unconfined Compressive Strength of Pervious Concrete Based on Mix Design and Compaction Energy Variables Using the Response Surface Methodology

Pervious concrete is desirable for water drainage in building systems, but achieving both high strength and good permeability can be challenging. Also, the importance of compaction energy is significant in determining the efficiency of pervious concrete. However, research on the development of unconfined compressive strength (UCS) prediction models for pervious concrete materials that incorporate compaction energy parameters remains unexplored. Therefore, this study aimed to balance strength and permeability while optimizing the compaction energy required for concrete production. A Central Composite Design (CCD) was used to design experiments within the response surface methodology (RSM) and evaluate the UCS, the porosity and permeability of pervious concrete specimens produced with varying cement content (280.00–340.00 kg/m3), the water-to-cement ratio (0.27–0.33), the aggregate-to-cement ratio (4:1–4.5:1), and compaction energy (represented by VeBe compaction time, 13–82 s). A regression model with goodness of fit (R2adjusted > 0.87) was calibrated to estimate the UCS of pervious concrete as a function of mix design parameters and VeBe compaction time (Tvc). This model can potentially guide field practices by recommending compaction strategies and mix designs for pervious concrete, achieving a desirable balance between mechanical strength and hydraulic permeability for building construction applications.

Explainable Ensemble Learning and Multilayer Perceptron Modeling for Compressive Strength Prediction of Ultra-High-Performance Concrete.

The performance of ultra-high-performance concrete (UHPC) allows for the design and creation of thinner elements with superior overall durability. The compressive strength of UHPC is a value that can be reached after a certain period of time through a series of tests and cures. However, this value can be estimated by machine-learning methods. In this study, multilayer perceptron (MLP) and Stacking Regressor, an ensemble machine-learning models, is used to predict the compressive strength of high-performance concrete. Then, the ML model's performance is explained with a feature importance analysis and Shapley additive explanations (SHAPs), and the developed models are interpreted. The effect of using different random splits for the training and test sets has been investigated. It was observed that the stacking regressor, which combined the outputs of Extreme Gradient Boosting (XGBoost), Category Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees regressors using random forest as the final estimator, performed significantly better than the MLP regressor. It was shown that the compressive strength was predicted by the stacking regressor with an average R2 score of 0.971 on the test set. On the other hand, the average R2 score of the MLP model was 0.909. The results of the SHAP analysis showed that the age of concrete and the amounts of silica fume, fiber, superplasticizer, cement, aggregate, and water have the greatest impact on the model predictions.

Predicting the compressive strength of recycled aggregate concrete using machine learning models

This paper investigates the compressive strength of recycled aggregate concrete (RAC) using machine learning models, specifically the Symbolic Regression (SR) and XGBoost models. The dataset consists of 1,047 experimental samples collected from 40 published studies, allowing for accurate prediction of compressive strength based on parameters such as cement content, water, fine aggregates, and recycled aggregates. The results show that the XGBoost model achieved high accuracy with an MAE of 4.65 MPa and an RMSE of 7.61 MPa. The paper also analyzes the influence of these parameters on compressive strength using the SHAP method, emphasizing the importance of understanding the correlations between variables to optimize recycled concrete design.

Compressive Strength Prediction of Basalt Fiber Reinforced Concrete Based on Interpretive Machine Learning Using SHAP Analysis

Compressive Strength Prediction of Basalt Fiber Reinforced Concrete Based on Interpretive Machine Learning Using SHAP Analysis

Uniaxial compressive strength prediction based on measurement while drilling data: A laboratory study

The uniaxial compressive strength is one of the most important basic parameters of rock, which is essential for surrounding rock stability analysis and support scheme design of underground engineering. At present, it is time consuming and costly to take a large amount of core samples for laboratory testing, and the mechanical properties of the cores may be affected by mining disturbance, which can easily lead to inaccurate result. The measurement while drilling (MWD) technology provides a new approach to solve the above challenge. The key to implementing this technology is to establish a correlation model between drilling parameters and rock mechanics parameters. Based on the characteristics of polycrystalline diamond compact (PDC) bits in drilling and rock breakage, this paper analyzes the mechanical state of the bit in breaking rock. A theoretical correlation model between the torque, feed force of the bit and the uniaxial compressive strength of the rock has been developed. To verify the accuracy of the theoretical model, the uniaxial compressive strength of five different types of rocks (red sandstone, green sandstone, limestone, marble and shale) was obtained through laboratory mechanical tests. The torque Mb, feed force Fb and other parameters in the drilling process of these five rocks were tested through the newly developed MWD test system. The correlation between the drilling parameters and the uniaxial compressive strength of rock was established. The results showed that the feed force Fb and torque Mb measured at five different types of rocks indicate a linear increasing trend with an increase in depth of cut h. Meanwhile, a strong linear relationship between the feed force Fb and torque Mb is evident. This paper proposes an MWD-based method to rapidly conduct the in-situ measurement of the uniaxial compressive strength of various rocks.

Compressive strength prediction of cement base under sulfate attack by machine learning approach

Cement-based materials in underground structures, bridge foundation, and offshore platforms often face sulfate environments. It has been widely reported that the compressive strength (CS) of these materials post sulfate attack is crucial for structural stability and durability. However, existing experimental and analytical methods require extensive resources and lengthy period. For addressing this challenge, therefore, a novel stacking model is proposed to accurately predict the CS of cement base under sulfate attack in this study. The stacking model integrates Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGB) as base learners, with Linear Regression (LR) as the meta-learner. In this novel model training, the 330 data sets used are obtained from the relevant literatures. The water-cement ratio, cement content, water content, sand content, sulfate concentration, wetting temperature, wetting time, drying temperature, drying time, exposure time are selected as inputs while the output is CS of cement-based materials subjected to sulphate deterioration. To improve the forecast capability, particle swarm optimization (PSO) is adopted for optimizing hyperparameters of stacking model, which can be implemented by reducing the discrepancy between model prediction and measured CS. By comparing with the results of experiments and the solely machine learning (ML) models, it is found that the stacking model optimized by PSO presents the best prediction accuracy of R²=0.992, RMSE=2.248 MPa, and MAE=1.782 MPa. Moreover, the examination of the influence of individual input variables on the CS of cement-based material subjected to sulfate attack is conducted through the application of Shapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses. The PSO optimized stacking model proposed in this study can effectively predict the CS of cement-based materials under sulfate attack. Our study provides a new reference for future research in this field.

Advancing earth science in geotechnical engineering: A data-driven soft computing technique for unconfined compressive strength prediction in soft soil

Advancing earth science in geotechnical engineering: A data-driven soft computing technique for unconfined compressive strength prediction in soft soil

Improving the experience of machine learning in compressive strength prediction of industrial concrete considering mixing proportions, engineered ratios and atmospheric features

In previous literature on predicting compressive strength (CS) using machine learning (ML), the focus has primarily been on algorithm-specific improvements, with less emphasis on improving the data perspective of CS prediction. Departing from these conventional investigations, this study presents data-centric enhancements. A systematic and comprehensive methodology is adopted, involving series of steps. Firstly, a large dataset (> 24,214 datapoints) comprising 26 input features relating to mixture proportions, engineered ratios and atmospheric parameters is prepared and processed. Feature engineering is then performed for selection and processing of data features. Subsequently, six scenarios are designed and investigated for CS prediction, focusing on distinct combinations of input features. Finally, CS predictions for all six scenarios are compared using five well-known ML model, and results are validated using statistical hypothesis testing. The results show significance improvements in CS prediction, with a 12.8 % increase in coefficient of determination (R2) and a 61 % reduction in root mean square error (RMSE) achieved by incorporating engineered and atmospheric parameters in conjunction with mixture proportion as inputs in CS prediction. Based on CS prediction in six scenarios, the study presents useful discussions focusing on concrete data perspective, role of engineering ratio and atmospheric features, and interaction and influence of input features in CS prediction. In conclusion, this work emphasizes the importance of enhancing data management practices for concrete performance assessment to have a more efficient, realistic, and sustainable outcomes in the concrete industry.

Prediction of concrete compressive strength using a Deepforest-based model

Concrete compressive strength testing is crucial for construction quality control. The traditional methods are both time-consuming and labor-intensive, while machine learning has been proven effective in predicting the compressive strength of concrete. However, current machine learning-based algorithms lack a thorough comparison among various models, and researchers have yet to identify the optimal predictor for concrete compressive strength. In this study, we developed 12 distinct machine learning-based regressors to conduct a thorough comparison and to identify the optimal model. To study the correlation between compressive strength and various factors, we conducted a comprehensive analysis and selected blast furnace slag, superplasticizer, age, cement, and water as the optimized factor subset. Based on this foundation, grid search and fivefold cross-validation were employed to establish the hyperparameters for each model. The results indicate that the Deepforest-based model demonstrates superior performance compared to the 12 models. For a more comprehensive evaluation of the model’s performance, we compared its performance with state-of-the-art models using the same independent testing dataset. The results demonstrate that our model achieving the highest performance (R2 of 0.91), indicating its accurate prediction capability for concrete compressive strength.

Enhancing the performances of self-consolidating composite mortars with Alfa fibers and slag: A combined Taguchi-ANN optimization

Alfa fiber-reinforced self-compacting mortars (SCMs) offer enhanced workability. Thus, the potential of combining natural Alfa fibers and ground granulated blast furnace slag (GBFS) to optimize the rheological and mechanical properties was ascertained. The effect of fibers and GBFS on the properties of SCMs after treatment with linseed oil and NaOH was also evaluated. Partial substitution of cement by slag (10 % and 30 %.) was achieved. Mini cone spreading, V-funnel flow tests as well as compressive and bending tests at 7 days and 28 days were performed on the prepared mixtures. For optimization and predictive purposes, both Taguchi optimization and Artificial Neural Network modeling were employed. Our results indicated that, according to Taguchi experimental design, the use of 1 % fiber in the initial mixture achieved a good distribution within the paste and improved flexural strength. In addition, the treatment of fibers with NaOH actually improved the structure and bonding surface. Fiber content and GBFS replacements achieved the desired rheological and mechanical characteristics. Prediction of both compressive and flexural strengths was successfully achieved by an accurate application of machine a learning algorithm based on six inputs, one hidden layer and four outputs. The combined ANN-Taguchi approach provided accurate predictions of SCM performances and identified a robust mix-design that was less prone to fluctuations in the initial material properties. Alfa fibers and GBFS promoted both fresh and hardened properties. This study demonstrated the effectiveness ML and statistical optimization in developing high-performance, sustainable-SCMs with superior workability and strengths, making them ideal for various construction applications.

Machine learning models for predicting the compressive strength of agro-waste stabilized bricks for sustainable buildings

Excessive consumption of time and resources are the major challenges of conducting laboratory experiments to determine the mechanical properties of building/construction materials. There is a need to explore various prediction models for mechanical properties of construction materials. This study is aimed at using machine learning models to predict the compressive strength of stabilized bricks for sustainable buildings. Data for the independent variables generated from laboratory experiments were used for the compressive strength prediction. Several machine learning models were explored using Lazy Predict Python library. Extreme Gradient Boosting Regressor with the coefficient of determination (R2) score of 99.45% and root mean square error of 0.06 outperformed all the tested models. SHAP (SHapley Additive exPlanations) analysis was used to show the distribution of the impacts of the features on the predicted compressive strength. The SHAP analysis results showed that higher percentages of the stabilizers (with reference to 0%, 2%, 4%, and 6% of stabilizers) have a positive impact on the prediction of compressive strength of stabilized bricks. In addition, higher water content, lower lateritic soil contents, lower cube sizes and curing at higher values of temperature favoured the prediction of compressive strength of stabilized bricks. The proposed model can be adapted by civil engineers and researchers for the prediction of the compressive strength of agro-waste stabilized construction materials. The implication of this study will save the time and resources required by the construction professionals to obtain the compressive strength agro-waste stabilized bricks during the production of bricks for sustainable construction applications.

Optimizing pervious concrete with machine learning: Predicting permeability and compressive strength using artificial neural networks

This study makes a significant contribution to the field of pervious concrete by using machine learning to innovatively predict both mechanical and hydraulic performance. Unlike existing methods that rely on labor-intensive trial-and-error experiments, our proposed approach leverages a multilayer perceptron network. To develop this approach, we compiled a comprehensive dataset comprising 271 sets and 3,252 experimental data points. Our methodology involved evaluating 22,246 network configurations, employing Monte Carlo cross-validation over 20 iterations, and using 4 training algorithms, resulting in a total of 1,779,680 training iterations. This results in an optimized model that integrates diverse mix design parameters, enabling accurate predictions of permeability and compressive strength even in the absence of experimental data, achieving R² values of 0.97 and 0.98, respectively. Sensitivity analyses validate the model's alignment with established principles of pervious concrete behavior. By demonstrating the efficacy of machine learning as a complementary tool for optimizing pervious concrete mix designs, this research not only addresses current methodological limitations but also lays the groundwork for more efficient and effective approaches in the field.

Optimizing compressive strength prediction using adversarial learning and hybrid regularization

The infrastructure industry consumes natural resources and produces construction waste, which has a detrimental impact on the environment. To mitigate these adverse effects and reduce raw material consumption, waste materials can be repurposed to achieve sustainability. However, recycled materials deteriorate the intrinsic properties of concrete. A suitable ratio of natural resources and recycled aggregates can produce the desired compressive strength. Compiling sufficient data in civil engineering laboratories to make reliable conclusions is time-consuming and costly. Therefore, this research proposes a novel approach for predicting compressive strengths using limited data. The generative adversarial network was employed to generate synthetic data. Hybrid training, utilizing either conventional loss or heuristic loss, prevents the model from overfitting by adaptively adjusting the regularization term. Random noise from a multivariate normal distribution is embedded heuristically into the training samples to capture intricate data variations. Sensitivity analysis indicated that the size of recycled coarse aggregate and water are the most significant features, aligning with their correlations. Interestingly, superplasticizer, density of recycled coarse aggregate, and water absorption ratio of recycled coarse aggregate contributed significantly to predictions despite their low correlations. The propounded method outperforms random forest, support vector regression, artificial neural network, and adaptive boosting by scoring a mean squared error of 7.97, a root mean squared error of 2.82, a mean absolute error of 2.13, and a coefficient of determination of 0.96. These results suggest that the proposed technique can effectively contribute to sustainable construction practices by accurately predicting compressive strengths.

AI-Driven Prediction of Compressive Strength in Self-Compacting Concrete: Enhancing Sustainability through Ultrasonic Measurements

This study investigates the application of artificial intelligence (AI) to predict the compressive strength of self-compacting concrete (SCC) through ultrasonic measurements, thereby contributing to sustainable construction practices. By leveraging advancements in computational techniques, specifically artificial neural networks (ANNs), we developed highly accurate predictive models to forecast the compressive strength of SCC based on ultrasonic pulse velocity (UPV) measurements. Our findings demonstrate a clear correlation between higher UPV readings and improved concrete quality, despite the general trend of decreased compressive strength with increased air-entraining admixture (AEA) concentrations. The ANN models show exceptional effectiveness in predicting compressive strength, with a correlation coefficient (R2) of 0.99 between predicted and actual values, providing a robust tool for optimizing SCC mix designs and ensuring quality control. This AI-driven approach enhances sustainability by improving material efficiency and significantly reducing the need for traditional destructive testing methods, thus offering a rapid, reliable, and non-destructive alternative for assessing concrete properties.

Novel prediction of unconfined compressive strength with least square support vector regression coupled with meta-heuristic optimizers

Novel prediction of unconfined compressive strength with least square support vector regression coupled with meta-heuristic optimizers

Development of a learner model tool for predicting strength and embodied carbon for lightweight concrete production

The demand for sustainable concrete in meeting the net zero carbon target places a burden in optimizing concrete response to structural strength that satisfy acceptable embodied carbon. In most cases, a low carbon concrete is deficient in structural requirement and vice versa. This dilemma informs the need for a tool that can predict compressive strength as well as embodied carbon using the same input data. Since the use of alternative materials as cement replacement to enhance sustainability is emerging in the quest for a sustainable concrete, an optimal material that satisfy both conditions of structural integrity and sustainability is still lacking. Paucity of data in the emerging lightweight low carbon concrete using alternative materials, portends an upheave in the bias for prediction of the behaviour of lightweight low carbon concrete. This study therefore uses concrete data of lightweight low carbon concrete from laboratory experiment for the prediction of compressive strength and embodied carbon with their performance evaluated using eight machine leaning regression models. The results obtained indicates that the XG boost regression model exhibited excellent performance with a low Mean Squared Error (MSE) of 50.15, Mean absolute error(MAE) = 5.26, Mean absolute percentage error(MAPE) = 11.76 %, Explained variance score = 0.97, Root mean square error(RMSE) = 7.08 and a high R squared value of 0.96. The tool predicted compressive strength and embodied carbon for lightweight carbon concrete using multiple output regression such that the output can be limited to the yearly structural embodied carbon threshold to achieving 2050 net zero target. The developed tool when compared with concrete of similar mix ingredients performed more than 95 % in predicting concrete compressive strength and associated embodied carbon. In line with the inclusion of embodied carbon in carbon regulations of buildings in the UK as suggested by the professionals in the construction industry, the developed learner model and prediction tool has integrated concrete strength and embodied carbon to initiate a holistic approach to design and construction, balancing performance, cost, and environmental impact.

Concrete Compressive Strength Prediction Using Combined Non-Destructive Methods: A Calibration Procedure Using Preexisting Conversion Models Based on Gaussian Process Regression

Non-destructive testing (NDT) techniques are crucial in making informed decisions about reconstructing or repairing building structures. The SonReb method, a combination of the rebound hammer (RH) and the ultrasonic pulse velocity (UPV) tests, is widely used for this purpose. To evaluate the compressive strength, CS, of the concrete under investigation, the ultrasonic pulse velocity Vp and the rebound index R must be mapped to the compressive strength CS using a suitable conversion model, the identification of which requires supplementing the NDT measurements with destructive-type measurements (DT) on a relatively large number of concrete cores. An approach notably indicated in all cases where the minimization of the number of cores is essential is to employ a pre-existing conversion model, i.e., a model derived from previous studies conducted in the literature, which must be appropriately calibrated. In this paper, we investigate the performance of Gaussian process regression (GPR) in calibrating the pre-existing SonReb conversion models, exploiting their ability to handle nonlinearity and uncertainties. The numerical results obtained using experimental data collected from the literature show that GPR calibration is very effective, outperforming, in most cases, the standard multiplicative and additive techniques used to calibrate the SonReb models.

Synthesis of geopolymer mortar from biomass ashes and forecasting its compressive strength behaviour

The global warming dilemma raised an exigency toward sustainability in the construction industry and compelled scientists to hunt for alternative binders in construction materials. The, geopolymerization as a spectacular technology has arisen as a looming solution to this dilemma. This work assesses the features of geopolymer mortar developed with both industrial and agricultural wastes at ambient temperature curing. The geopolymer mix proportion was evolved in this study using main precursor material as fly ash, ground granulated blast furnace slag (GGBFS), sugarcane bagasse ash (SCBA) or rice husk ash (RHA). Workability and mechanical properties, including compressive strength, flexural strength, and split tensile strength of different mortar mixes with various percentages of RHA and SCBA for 365 days, were evaluated. Geopolymer mortar with 10 % SCBA and 5 % RHA achieved improvements of 25 % and 13.91 % in compressive strength, 10.6 % and 3.5 % in flexural strength, and 35 % and 16.8 % in split tensile strength, respectively. Good geopolymer gel formation is clearly evident from the SEM images of SCBA mortar. The main polymeric bonds Si–O–Al and Si-O-Si are also identified using FTIR. To assess the efficacious use of biomass ashes, resistance to chemical attack using H2SO4, HCl, Na2SiO3, and NaCl solutions for 365 days was studied. It was revealed that SCBA incorporated geopolymer mortar specimens achieved good resistance compared with RHA geopolymer mortar. The relation between the ambient temperature cured geopolymer specimen compressive strength at 28 days and 365 days chemical solution curried geopolymer specimen compressive strength find out using python and obtained R2 values more than 95 %. Also, this research proposed various machine learning techniques such as long short-term memory, support vector regression, random forest regression, XGBOOST, and AdaBOOST algorithms for the prediction of compressive strength of geopolymer using 724 mixture proportions with diverse curing temperatures and varying ages. The XGBOOST algorithm achieved the highest R² value of 0.92, demonstrating its effectiveness for the strength prediction.