Support Vector Regression Model Research Articles

Prediction of Floor Failure Depth in Coal Mines: A Case Study of Xutuan Mine, China

Accurately predicting the failure depth of coal seam floors is crucial for preventing water damage, ensuring the safe and efficient mining of coal seams, and protecting the ecological environment of mining areas. In order to improve the prediction accuracy of the coal seam floor failure depth, an improved support vector regression (SVR) model is proposed to predict the floor failure depth by taking the 3234 working face in Xutuan Mine as an example. This improved model incorporates principal component analysis (PCA) and slime mould algorithm (SMA) optimization techniques. First, based on the measured data of seam floor failure depth in several mining areas, a prediction index system of floor failure depth was constructed. Subsequently, the PCA method was used to reduce the dimension of the measured data of the coal seam floor failure depth, and the input structure of the SVR model was optimized. Then, the SMA was used to optimize the key parameters, namely the penalty factor (C) and kernel function parameter (g), in the SVR model, achieving automatic parameter selection and obtaining the optimal parameter combination. This process led to the establishment of a coal seam floor failure depth prediction model based on PCA-SMA-SVR. The predictive performance of the PCA-SMA-SVR model, SMA-SVR model, and SVR model was quantitatively evaluated and compared using four quantitative indicators, and the results showed that the PCA-SMA-SVR model had the smallest MAE, RMSE, MRE, and TIC values, which were 1.0470 m, 1.2928 m, 0.0628, and 0.0374, respectively. Finally, the PCA-SMA-SVR model was used to predict that the floor failure depth of the 3234 working face in Xutun Mine was 17.09 m, and the predicted result was compared and analyzed with the results of four commonly used empirical formulas (16.03–21.74 m). The results show that the model is close to the results of four commonly used empirical formulas, indicating that the model has high predictive performance and good practicality. This study is of great significance for the safety, green mining, and ecological environment protection of coal mines.

Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression Model

Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales.

Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study

Background: Three-dimensional gait analysis, supported by advanced sensor systems, is a crucial component in the rehabilitation assessment of post-stroke hemiplegic patients. However, the sensor data generated from such analyses are often complex and challenging to interpret in clinical practice, requiring significant time and complicated procedures. The Gait Deviation Index (GDI) serves as a simplified metric for quantifying the severity of pathological gait. Although isokinetic dynamometry, utilizing sophisticated sensors, is widely employed in muscle function assessment and rehabilitation, its application in gait analysis remains underexplored. Objective: This study aims to investigate the use of sensor-acquired isokinetic muscle strength data, combined with machine learning techniques, to predict the GDI in hemiplegic patients. This study utilizes data captured from sensors embedded in the Biodex dynamometry system and the Vicon 3D motion capture system, highlighting the integration of sensor technology in clinical gait analysis. Methods: This study was a cross-sectional, observational study that included a cohort of 150 post-stroke hemiplegic patients. The sensor data included measurements such as peak torque, peak torque/body weight, maximum work of repeated actions, coefficient of variation, average power, total work, acceleration time, deceleration time, range of motion, and average peak torque for both flexor and extensor muscles on the affected side at three angular velocities (60°/s, 90°/s, and 120°/s) using the Biodex System 4 Pro. The GDI was calculated using data from a Vicon 3D motion capture system. This study employed four machine learning models—Lasso Regression, Random Forest (RF), Support Vector regression (SVR), and BP Neural Network—to model and validate the sensor data. Model performance was evaluated using mean squared error (MSE), the coefficient of determination (R2), and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Results: The RF model outperformed others in predicting GDI, with an MSE of 16.18, an R2 of 0.89, and an MAE of 2.99. In contrast, the Lasso Regression model yielded an MSE of 22.29, an R2 of 0.85, and an MAE of 3.71. The SVR model had an MSE of 31.58, an R2 of 0.82, and an MAE of 7.68, while the BP Neural Network model exhibited the poorest performance with an MSE of 50.38, an R2 of 0.79, and an MAE of 9.59. SHAP analysis identified the maximum work of repeated actions of the extensor muscles at 60°/s and 120°/s as the most critical sensor-derived features for predicting GDI, underscoring the importance of muscle strength metrics at varying speeds in rehabilitation assessments. Conclusions: This study highlights the potential of integrating advanced sensor technology with machine learning techniques in the analysis of complex clinical data. The developed GDI prediction model, based on sensor-acquired isokinetic dynamometry data, offers a novel, streamlined, and effective tool for assessing rehabilitation progress in post-stroke hemiplegic patients, with promising implications for broader clinical application.

Dengue Prediction using Short-Term Memory

Dengue is a severe infectious disease on the rise in Malaysia, and there is a demand for artificial intelligence to support the health system. However, the application of deep learning, specifically Long Short-Term Memory (LSTM) time series forecasting, has not been explored by many in dengue prediction studies. However, considering the availability of daily weather data being collected, the ability of LSTM to capture long term dependencies can be leveraged in forecasting dengue cases. Therefore, this study investigates the performance and viability of LSTM time series forecasting on predicting dengue cases. An LSTM model is developed and evaluated to be compared to a Support Vector Regression (SVR) model by utilising the availability of a dengue dataset consisting of weather and climate data. The results indicated LSTM time series forecasting performed better than SVR, with R2 and MAE scoring 0.75 and 8.76. In short, LSTM has shown better performance and, in addition, capturing trends in the rise and fall of dengue cases. Altogether, this research could contribute to the fight against the increase of dengue cases without relying on forecasted weather data but instead, history.

Process–Material–Performance Trade-off Exploration of Materials Sintering with Machine Learning Models

AbstractProcess-induced porosity, defects, and residual stresses lead to mechanical performance degradation in fiber-reinforced composite and other heterogeneous structures. Physical and chemical processes create complex process–material–performance relationships. Predicting porosity and residual stresses in this context requires computationally burdensome forward simulations and obtaining optimal process settings and calibrating properties of new materials requires solving inverse problems with predictions from the forward simulations. In this paper, we parameterized the process–material–performance space and created a dataset based on physics models that are valid for sintering ceramic powders. The dataset was used to train several machine learning models that captured the process–material–performance relationships. The trained ML models were applied in process optimization, calibration of properties for new material systems, and estimating performance for a given process and material. Support vector regression (SVR), convolutional neural networks (CNNs), and a Gaussian mixture model (GMM) called REPAIRS were selected, and their prediction accuracy was determined. While the SVR and CNN models require training several models, we show that the GMM model captures the process–material–performance relationships with a single machine-learned model and partial system completion methods. The paper describes root-mean-square error and mean absolute percentage errors of the inferences from the models on a validation dataset.

Acoustic absorption characterization and modeling using support vector regression of plasterboards reinforced with woven Moroccan sisal fiber

The acoustic comfort and environmental concerns in the building sector are two interconnected and crucial aspects for the sustainable development of the construction industry. This study investigates the acoustic absorption properties of plasterboards reinforced with woven fabric made from Moroccan sisal fiber. Three different weave designs, Basket 2-2, 2/2 Twill, and 4-Harness Satin, were produced using a conventional weaving process. Plasterboards were manually fabricated by varying the number of fabric layers (one, two, or three) and employing a water/plaster ratio of 0.7. The acoustic performance of the plasterboards was evaluated using an impedance tube according to ISO 10534-2. The results demonstrated that the plasterboards reinforced with the Basket 2-2 weave achieved the highest sound absorption coefficient, reaching 0.39 at 1700 Hz with three layers, significantly higher than pure plasterboards, which exhibited a maximum coefficient of 0.05. The study also employed support vector regression (SVR) models to predict the sound absorption coefficient, with the linear SVM model achieving the best predictive accuracy (R² = 0.88, RMSE = 0.024). These findings underscore the potential of Moroccan sisal fiber as an eco-friendly reinforcement for improving the acoustic absorption of plasterboards.

COMPARATIVE ANALYSIS OF RF, SVR WITH GAUSSIAN KERNEL AND LSTM FOR PREDICTING LOAN DEFAULTS

This investigation elucidates the paramount endeavour of predicting loan defaults, which is imperative for the efficacious management of financial risk and the overall stability of financial institutions. Conventional statistical methodologies frequently encounter challenges in effectively capturing the nonlinear and sequential dynamics inherent in financial data, thereby necessitating the examination of more sophisticated machine learning methodologies. This research reports an experimental-based comparative evaluation of three ML and DL models—Long Short-Term Memory (LSTM) networks, Random Forest (RF), and Support Vector Regression (SVR)—to assess their efficacy in forecasting loan defaults. The models are evaluated using metrics such as Mean Squared Error (MSE), F1 score, and Accuracy, and their proficiency in addressing imbalanced datasets and elucidating intricate data relationships is highlighted. The results indicate that while the Random Forest model surpasses its counterparts in terms of accuracy and MSE, the LSTM model exhibits considerable potential in managing imbalanced data, as evidenced by its stable F1 score. Although SVR reveals competitive precision, it exhibits deficiencies in addressing class imbalance. The ANOVA analyses substantiate that the disparities in model performance are statistically significant. The research acknowledges that both the LSTM and SVR models remain in the developmental stages, with ongoing initiatives aimed at refining these models through hyperparameter optimization and advanced architectural frameworks to enhance their predictive efficacy in practical applications.

Enhancing Soil Salinity Evaluation Accuracy in Arid Regions: An Integrated Spatiotemporal Data Fusion and AI Model Approach for Arable Lands

Soil salinization is one of the primary factors contributing to land degradation in arid areas, severely restricting the sustainable development of agriculture and the economy. Satellite remote sensing is essential for real-time, large-scale soil salinity content (SSC) evaluation. However, some satellite images have low temporal resolution and are affected by weather conditions, leading to the absence of satellite images synchronized with ground observations. Additionally, some high-temporal-resolution satellite images have overly coarse spatial resolution compared to ground features. Therefore, the limitations of these spatiotemporal features may affect the accuracy of SSC evaluation. This study focuses on the arable land in the Manas River Basin, located in the arid areas of northwest China, to explore the potential of integrated spatiotemporal data fusion and deep learning algorithms for evaluating SSC. We used the flexible spatiotemporal data fusion (FSDAF) model to merge Landsat and MODIS images, obtaining satellite fused images synchronized with ground sampling times. Using support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models, we evaluated the differences in SSC evaluation results between synchronized and unsynchronized satellite images with ground sampling times. The results showed that the FSDAF model’s fused image was highly similar to the original image in spectral reflectance, with a coefficient of determination (R2) exceeding 0.8 and a root mean square error (RMSE) below 0.029. This model effectively compensates for the missing fine-resolution satellite images synchronized with ground sampling times. The optimal salinity indices for evaluating the SSC of arable land in arid areas are S3, S5, SI, SI1, SI3, SI4, and Int1. These indices show a high correlation with SSC based on both synchronized and unsynchronized satellite images with ground sampling times. SSC evaluation models based on synchronized satellite images with ground sampling times were more accurate than those based on unsynchronized images. This indicates that synchronizing satellite images with ground sampling times significantly impacts SSC evaluation accuracy. Among the three models, the CNN model demonstrates the highest predictive accuracy in SSC evaluation based on synchronized and unsynchronized satellite images with ground sampling times, indicating its significant potential in image prediction. The optimal evaluation scheme is the CNN model based on satellite image synchronized with ground sampling times, with an R2 of 0.767 and an RMSE of 1.677 g·kg−1. Therefore, we proposed a framework for integrated spatiotemporal data fusion and CNN algorithms for evaluating soil salinity, which improves the accuracy of soil salinity evaluation. The results provide a valuable reference for the real-time, rapid, and accurate evaluation of soil salinity of arable land in arid areas.

Communication—Prediction of Area Specific Resistance of Solid Oxide Cell Stacks from Electrochemical Impedance Spectra

A support vector regression model was trained to predict the area specific resistance of solid oxide cell stacks from electrochemical impedance spectroscopy measurements. In particular, the minimum necessary frequency range required for accurate predictions was investigated. Therefore, 711 pairs of DC polarization and electrochemical impedance spectroscopy measurements, conducted at the same stage of stack degradation and under similar measurement conditions, were evaluated. Prediction accuracies of less than 8% mean absolute percentage error and coefficients of determination greater than 93% were achieved. The 101–102 Hz range is sufficient for accurate predictions, allowing an efficient and non-destructive etimation of DC polarization measurements.

Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model

Our study investigates the use of machine learning models for daily precipitation prediction using data from 56 meteorological stations in Jilin Province, China. We evaluate Stacked Long Short-Term Memory (LSTM), Transformer, and Support Vector Regression (SVR) models, with Stacked-LSTM showing the best performance in terms of accuracy and stability, as measured by the Root Mean Square Error (RMSE). To improve robustness, Gaussian noise was introduced, particularly enhancing predictions for zero-precipitation days. Key predictors identified through variable attribution analysis include temperature, dew point, prior precipitation, and air pressure. Additionally, we demonstrate the practical benefits of precipitation forecasts in optimizing water resource allocation. A prediction-based strategy outperforms equal distribution in managing resources efficiently, as shown in a case study using 2022 Beidahu data. Overall, our research advances precipitation forecasting through deep learning and offers valuable insights for water resource management.

Research on extraction and identification of automotive wind noise based on LSTM combined road and drum experiments

Wind noise is a significant source of noise when vehicles are driven at high speeds. While wind tunnel experiments are commonly method to study real automotive wind noise, anechoic wind tunnel laboratory are prohibitive for automakers on account of their high costs and limited experimental period. Therefore, this paper proposes to extract automotive wind noise through combining road experiment and drum experiment, define significant wind noise prime regions, and verify its effectiveness. Subsequently, the Long Short-Term Memory Neural Network algorithm (LSTM) is employed to reveal the complex nonlinear relationship between wind noise and its impact areas, in order to establish an identification model for automotive wind noise. The performance of the model is then compared with Backpropagation Neural Networks (BPNN) and Support Vector Regression (SVR) models. The results indicate that the LSTM wind noise identification model exhibits higher accuracy, shorter training time, and stronger generalization ability, thus demonstrating the superiority of this model.

Optimising Neural Networks for Enhanced Fracture Density Prediction in Surrounding Rock of Coalbed Methane Reservoir

ABSTRACTFractures influence the mechanical strength of coal roof and floor, constraining the design of hydraulic fracturing for coalbed methane production. Currently, the predominant approach involves the integration of petrophysical logging with machine learning for fracture prediction. Nevertheless, challenges exist regarding the model's accuracy. In this study, we present a novel approach to predict fracture density. Our method optimises a back‐propagation (BP) neural network and utilises principal component analysis for feature extraction. We employ logging parameters (density, compensated neutron and acoustic time difference) obtained from Shouyang Block well SY‐1 and fracture density data from electrical imaging logging to construct the FVDC model's dataset. The BP neural network model is optimised using the Sparrow Search algorithm and Tent Chaotic Mapping. The results demonstrate a substantial enhancement over the BP neural network model, with reductions of 80.102% in mean absolute error, 94.182% in mean square error, 75.879% in root mean square error and 79.764% in mean absolute percentage error. When considering accuracy, the optimised model (97.098%) surpasses the support vector regression model (96.478%), the random forest model (94.404%) and the BP neural network model (85.657%). Scalability testing for the optimised model was conducted using data from well SY‐2, yielding a remarkable prediction accuracy of 96.775%. This performance exceeds that of the BP neural network (with an accuracy of 85.102%), as well as the random forest and support vector regression models (with accuracies of 91.234% and 90.384%, respectively). These results underscore the potential of well logging and machine learning in FVDC prediction.

Optimization of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility

In this study, a landslide susceptibility assessment is performed by combining two machine learning regression algorithms (MLRA), such as support vector regression (SVR) and categorical boosting (CatBoost), with two population-based optimization algorithms, such as grey wolf optimizer (GWO) and particle swarm optimization (PSO), to evaluate the potential of a relatively new algorithm and the impact that optimization algorithms can have on the performance of regression models. The Kerala state in India has been chosen as the test site due to the large number of recorded incidents in the recent past. The study started with 18 potential predisposing factors, which were reduced to 14 after a multi-approach feature selection technique. Six susceptibility models were implemented and compared using the machine learning algorithms alone and combining each of them with the two optimization algorithms: SVR, CatBoost, SVR-PSO, CatBoost-PSO, SVR-GWO, and CatBoost-GWO. The resulting maps were validated with an independent dataset. The performance rankings, based on the area under the receiver operating characteristic curve (AUC) metric, are as follows: CatBoost-GWO (AUC = 0.910) had the highest performance, followed by CatBoost-PSO (AUC = 0.909), CatBoost (AUC = 0.899), SVR-GWO (AUC = 0.868), SVR-PSO (AUC = 0.858), and SVR (AUC = 0.840). Other validation statistics corroborated these outcomes, and the Friedman and Wilcoxon-signed rank tests verified the statistical significance of the models. Our case study showed that CatBoost outperformed SVR both in case the models were optimized or not; the introduction of optimization algorithms significantly improves the results of machine learning models, with GWO being slightly more effective than PSO. However, optimization cannot drastically alter the results of the model, highlighting the importance of setting up of a rigorous susceptibility model since the early steps of any research.

Forecasting Orange Juice Futures: LSTM, ConvLSTM, and Traditional Models Across Trading Horizons

This study evaluated the forecasting accuracy of various models over 5-day and 10-day trading horizons to predict the prices of orange juice futures (OJ = F). The analysis included traditional models like Autoregressive Integrated Moving Average (ARIMA) and advanced neural network models such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), and Convolutional Long Short-Term Memory (ConvLSTM), incorporating factors like the Commodities Index and the S&P500 Index. We employed loss function metrics and various tests to assess model performance. The results indicated that for the 5-day horizon, the LSTM and ConvLSTM consistently outperformed the other models. LSTM achieved the lowest error rates and demonstrated superior capability in capturing temporal dependencies, especially in single-factor and S&P500 Index predictions. ConvLSTM also performed strongly, effectively modeling spatial and temporal data patterns. In the 10-day horizon, similar trends were observed. LSTM and ConvLSTM models had significantly lower errors and better alignment with actual values. The BPNN model performed well when all factors were included, and the SVR model maintained consistent accuracy, particularly for single-factor predictions. The Diebold–Mariano (DM) test indicated significant differences in forecasting accuracy, favoring advanced neural network models. In addition, incorporating multiple influencing factors further improved predictive performance, enhancing investment outcomes and reducing risk.

Strong robust state of health estimation of lithium-ion batteries based on aging feature mechanism analysis and improved mixed kernel least squares support vector regression model

Strong robust state of health estimation of lithium-ion batteries based on aging feature mechanism analysis and improved mixed kernel least squares support vector regression model

Cargo Volume Forecasting for Logistics Sorting Centers Based on Random Forest Model

With the rapid development of logistics industry, logistics sorting center, as a key node in e-commerce logistics network, its cargo volume prediction and personnel scheduling strategy are of great significance to improve operational efficiency and reduce costs. The purpose of this study is to deeply analyze the cargo volume data of 57 sorting centers through machine learning method, establish an accurate cargo volume prediction model, and optimize the personnel scheduling strategy based on the prediction results. In this study, we preprocess the historical cargo data, including missing value filling, time series conversion and feature construction. Then, the random forest model was used to carry out fitting analysis on the cargo volume data of different sorting centers. By comparing the evaluation indexes such as mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE), the superiority of random forest model compared with neural network, support vector regression and linear regression models was verified. This study not only improves the accuracy of cargo volume forecast, but also realizes the reasonable allocation of human resources, and provides scientific decision support for the operation and management of logistics sorting center.

Insights into Tetravalent Np Speciation in HNO3 through Spectroelectrochemistry and Multivariate Analysis.

In situ optical spectroscopy, spectropotentiometry, and multivariate analysis were applied to the Np(IV) nitrate system to better understand speciation and quantify HNO3 concentration. Thin-layer spectropotentiometry, or spectroelectrochemistry, was leveraged to isolate and stabilize Np(IV) without compromising the solution conditions and generate representative Vis-NIR absorption spectra from 0.5 to 10 M HNO3 and benchmark the corresponding Np(IV) molar absorptivity coefficients. Spectra were described with principal component analysis (PCA) to identify the purest Np(IV) absorbance spectra among other oxidation states [e.g., Np(V/VI)] at each acid concentration and then to identify the primary sources of variance within each Np(IV) spectrum with respect to Np(IV) nitrate complexes. Then, partial least-squares regression (PLSR) and support vector regression (SVR) models were built to predict HNO3 concentration from the Np(IV) spectral data. The nonlinear SVR model outperformed the linear PLSR model for the HNO3 concentration predictions. Finally, the inclusion of spectra collected in edge and center point HNO3 concentrations in the calibration set was determined to be crucial for producing models with strong predictive capabilities. The multivariate approach used in this study makes it possible to quantify HNO3 concentration solely based on Np(IV) absorption spectra, which is essential to quantifying processing streams in various online monitoring applications.

Investigation of age-hardening behaviour of Al alloys via feature screening-assisted machine learning

Age hardening stands as a crucial strengthening process for aluminium (Al) alloys. However, determining the optimal ageing conditions has traditionally relied on resource-intensive trial-and-error methods. To streamline the design of Al alloys, this study introduces a novel feature screening-assisted machine learning (ML) approach to explore age-hardening behaviour across varying compositions and heat treatment parameters, focusing on yield tensile strength (YTS), ultimate tensile strength (UTS), and elongation (EL). A comprehensive pool of features, incorporating alloy composition and fundamental elemental properties, was generated to provide metallurgical insights for ML predictions. Subsequently, feature screening methodologies, including correlation analysis, feature elimination, and multi-objective genetic algorithm (MOGA), were employed to identify key features from the vast feature pool, balancing model complexity and prediction accuracy. Employing support vector regression models trained on these optimized feature sets, we demonstrate enhanced prediction accuracy for strength properties while maintaining model simplicity for EL, with minimal impact on accuracy. In addition, experimental results further validate the method's ability to reproduce ageing curves across all three mechanical properties for both commercialized and newly designed Al alloys.

A flexible and efficient algorithm for high dimensional support vector regression

In high dimensional statistical learning, variable selection and handling highly correlated phenomena are two crucial topics. Elastic-net regularization can automatically perform variable selection and tends to either simultaneously select or remove highly correlated variables. Consequently, it has been widely applied in machine learning. In this paper, we incorporate elastic-net regularization into the support vector regression model, introducing the Elastic-net Support Vector Regression (En-SVR) model. Due to the inclusion of elastic-net regularization, the En-SVR model possesses the capability of variable selection, addressing high dimensional and highly correlated statistical learning problems. However, the optimization problem for the En-SVR model is rather complex, and common methods for solving the En-SVR model are challenging. Nevertheless, we observe that the optimization problem for the En-SVR model can be reformulated as a convex optimization problem where the objective function is separable into multiple blocks and connected by an inequality constraint. Therefore, we employ a novel and efficient Alternating Direction Method of Multipliers (ADMM) algorithm to solve the En-SVR model, and provide a complexity analysis as well as convergence analysis for the algorithm. Furthermore, extensive numerical experiments validate the outstanding performance of the En-SVR model in high dimensional statistical learning and the efficiency of this novel ADMM algorithm.

A machine learning model for predicting the mechanical strength of cement-based materials filled with waste rubber modified by PVA

As demand for sustainable building materials rises, the use of waste rubber in civil engineering is gaining attention. This study proposes a method to modify waste rubber using polyvinyl alcohol (PVA) to enhance its material properties and expand its applications. A dataset was created focusing on the mechanical strength of cementitious materials incorporating PVA-modified waste rubber, and multiple machine learning methods were used to develop regression prediction models, particularly evaluating the support vector regression (SVR) model. Results show that the SVR model outperforms others, achieving mean squared errors of 1.21 and 0.33, and mean absolute errors of 2.06 and 0.15. Analysis indicates a negative correlation between waste rubber content and the water-to-cohesive ratio (w/c) with strength indexes, while a positive correlation exists between curing age and PVA. Notably, waste rubber content significantly affects strength. The mechanical strength of cementitious materials was notably enhanced by PVA-modified waste rubber, likely due to PVA's dispersion and bridging effects. This study presents a novel approach to sustainably recycle waste rubber, highlighting its potential in construction materials.