Seismic response assessment requires reliable information about subsurface conditions, including soil shear wave velocity (Vs). To properly assess seismic response, engineers need accurate information about Vs, an essential parameter for evaluating the propagation of seismic waves. However, measuring Vs is generally challenging due to the complex and time-consuming nature of field and laboratory tests. This study aims to predict Vs using machine learning (ML) algorithms from cone penetration test (CPT) data. The study utilized four ML algorithms, namely Random Forests (RFs), Support Vector Machine (SVM), Decision Trees (DT), and eXtreme Gradient Boosting (XGBoost), to predict Vs. These ML models were trained on 70% of the datasets, while their efficiency and generalization ability were assessed on the remaining 30%. The hyperparameters for each ML model were fine-tuned through Bayesian optimization with k-fold cross-validation techniques. The performance of each ML model was evaluated using eight different metrics, including root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R2), performance index (PI), scatter index (SI), A10−I, and U95. The results demonstrated that the RF model consistently performed well across all metrics. It achieved high accuracy and the lowest level of errors, indicating superior accuracy and precision in predicting Vs. The SVM and XGBoost models also exhibited strong performance, with slightly higher error metrics compared with the RF model. However, the DT model performed poorly, with higher error rates and uncertainty in predicting Vs. Based on these results, we can conclude that the RF model is highly effective at accurately predicting Vs using CPT data with minimal input features.
Expansive soils undergo high volume change due to cyclic swelling and shrinkage behavior during the wet and dry seasons. Thus, such problematic soils should be completely avoided or properly treated when encountered as subgrade materials. In the present study, the biomedical waste incinerator ash and lime combination was proposed to stabilize expansive soil. Particle size analysis, Atterberg limits, free-swell, compaction, unconfined compression strength, and California bearing ratio tests were conducted on the natural soil and blended with 3%, 5%, 7%, 9%, and 11% biomedical waste incinerator ash (BWIA). The optimum content of BWIA was determined based on the free-swell test results. To further investigate the relative effectiveness of the stabilizer, 2% and 3% lime were also added to the optimum soil-BWIA mixture and UCS and CBR tests were also conducted. In addition, scanning electron microscopy (SEM) tests for representative stabilized samples were also conducted to examine the changes in microfabrics and structural arrangements due to bonding. The addition of BWIA has a promising effect on the index properties and strength of the expansive soil. The strength of the expansive soil significantly increased when it was blended with the optimum content of BWIA amended by 2% and 3% lime.
Conventional soil classification methods are expensive and demand extensive field and laboratory work. This research evaluates the efficiency of various machine learning (ML) algorithms in classifying soils based on Robertson’s soil behavioral types. This study employs 4 ML algorithms, including artificial neural network (ANN), random forest (RF), support vector machine (SVM), and decision trees (DT), to classify soils from 232 cone penetration test (CPT) datasets. The datasets were randomly split into training and testing datasets to train and test the ML models. Metrics such as overall accuracy, sensitivity, precision, F1_score, and confusion matrices provided quantitative evaluations of each model. Our analysis showed that all the ML models accurately classified most soils. The SVM model achieved the highest accuracy of 99.84%, while the ANN model achieved an overall accuracy of 98.82%. The RF and DT models achieved overall accuracy scores of 99.23% and 95.67%, respectively. Additionally, most of the evaluation metrics indicated high scores, demonstrating that the ML models performed well. The SVM and RF models exhibited outstanding performance on both majority and minority soil classes, while the ANN model achieved lower sensitivity and F1_score for minority soil class. Based on these results, we conclude that the SVM and RF algorithms can be integrated into software programs for rapid and accurate soil classification.
The cone penetration test (CPT) has been the de facto field exploration method in geotechnical engineering for decades. Variations of CPT can measure parameters for seismic, environmental, and hydrological applications. Analyzing response often requires properties in areas that have little or no data. Therefore, given the limited CPT data, it is critical to understand how to accurately estimate the soil properties at unsampled locations. In this paper, we generated soil shear wave velocity profiles using the kriging interpolation technique and assessed their performance using site response analysis. Four kriging interpolation-based shear wave velocity profiles and four additional CPT-based shear wave velocity profiles defined site conditions for response analysis. We performed a series of 1-D equivalent linear site response analyses using STRATA software. The site response analysis results are presented as amplification factors (AF), peak ground acceleration (PGA) profiles, surface spectral acceleration, and surface acceleration time histories. Compared to CPT-based profiles, the results of kriging interpolation-based profiles were evaluated and discussed. The results confirmed the reliability of the kriging interpolation technique in predicting soil parameters at unsampled locations.
Dynamic cone penetration testing has been extensively used in the past for subgrade performance assessment and quality control in road construction practice. However, the method is not commonly employed on high-speed railways. This is due to lack of field data that prove its feasibility as an alternative method for assessing subgrade quality. To mitigate this gap, a series of in situ tests was performed on existing subgrades built with coarse-grained soils at five different sections along the Tehran–Isfahan high-speed railway in Iran. At each subgrade section tested, four parameters for compaction quality control – blow count, degree of compaction, subgrade reaction modulus and dynamic deformation modulus – were determined at nine different depths from subgrade surface. On the basis of the results obtained, a correlation model was developed to relate the traditional quality control parameters of compacted subgrade fill materials with the blow counts. Finally, a simple method using the correlation models established was proposed for assessment of subgrade compaction quality. The method proposed proved to be an alternative approach for evaluating the state of subgrade compaction and also for assessing the subgrade performance of existing railway subgrades.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.