A data-driven machine learning framework to accurately predict differential settlement at the junction of existing and new embankments is developed, offering a tool for rapid performance prediction and damage assessment that significantly outperforms conventional simulation-based methods in speed and efficiency.
Abstract
Differential settlement at the junction of existing and new embankments is a critical challenge in road-widening projects. This study develops a data-driven machine learning (ML) framework to accurately predict this settlement, overcoming the limitations of traditional finite element methods. A comprehensive dataset was generated with finite element simulations (iSight-ABAQUS) and used to train and evaluate eight ML algorithms. Among these, the Gradient Boosting model demonstrated superior performance, achieving an R² value approaching 1.0, indicating exceptional predictive accuracy and generalization. The finalized model was implemented in a Python-based framework, enabling rapid forecasting of road behavior. This ML approach facilitates accelerated design optimization, identifies key influencing parameters like the modulus of elasticity, and supports proactive maintenance planning. The research establishes a transformative, data-driven paradigm for pavement engineering, offering a tool for rapid performance prediction and damage assessment that significantly outperforms conventional simulation-based methods in speed and efficiency.
Reliable forecasting of drilling rate of penetration (ROP) remains a technical challenge due to its complex dependence on several operational, geological, and directional conditions that are highly nonlinear and well-specific. These difficulties are amplified in deviated wells, where traditional empirical relations often fail to capture the combined effects of lithological variability and directional changes. This study develops a machine learning (ML) model for ROP prediction using multi-well field data and evaluates candidate ML algorithms/models not only on numerical accuracy but also on their ability to reproduce reasonable trend responses to changes in key drilling parameters. The objective is to establish a validation approach that integrates statistical performance, cross-well generalization, and sensitivity behavior consistent with drilling mechanics.
Field data from 18 wells were used to develop the model, while two additional wells were reserved for independent validation. A total of 91,140 drilling datasets, comprising 24 parameters/features, were collected. After preprocessing and selective outlier screening, 85,695 records were retained for model training and testing. Feature selection resulted in nine highly influential parameters: True Vertical Depth (TVD), Weight on Bit (WOB), rotational speed (Rotation), mud flow rates (FLOW), mud density, shale content (Shale), Sandstone content (Sandstone), inclination (Inc), and dogleg severity (DLS). Eleven supervised ML algorithms were evaluated, representing instance-based, tree-based, boosting-based, neural-network, and stacking-ensemble model families. Model performance was assessed using feature-importance ratings and statistical metrics, including train/test R2, MSE, RMSE, and MAE, which were used to select the best-performing model. Finally, sensitivity analysis was conducted to evaluate the selected model's ability to reproduce physically meaningful ROP responses.
Model comparison across the training and testing splits demonstrated strong overall predictive performance, with training R2 values ranging from 0.91 to 0.99 and testing R2 values ranging from 0.88 to 0.94. Among the evaluated models, the Gradient Boosting (GB) model provided the best overall performance, with training R2 = 0.9940, testing R2 = 0.9399, RMSE = 36.88, and MAE = 21.52. The GB model also showed strong agreement with the averaged feature-importance ranking and reproduced physically meaningful ROP sensitivity trends for the dominantly influential drilling parameters. SHAP and feature-importance analyses confirmed that TVD, FLOW, WOB, shale content, and rotation were the most influential variables controlling ROP. External validation on two unseen wells further demonstrated strong generalization, with R2 = 0.9694 for Well-19 and R2 = 0.96 for Well-20.
This study presents a physically informed framework for evaluating and selecting data-driven ML models for deviated wells. The approach combines conventional accuracy metrics with feature-importance consistency, reasonable trend prediction in sensitivity analysis, and strong agreement between model predictions and unseen-well data. Through this integrated evaluation, a model is identified that not only reproduces historical data accurately but also yields correct responses under a diverse range of varying input parameters. The proposed methodology establishes a reproducible basis for developing more reliable ROP forecasting tools for complex well trajectories.
Nayem Ahmed, Ramadan Ahmed, V. Soriano et al.· SPE/IADC Asia Pacific Drilli...· 0 citations
Accurate prediction of the residual drift ratio of reinforced concrete bridge piers is challenging because conventional methods are computationally expensive, time-consuming, and unable to effectively capture complex nonlinear interactions among multiple influencing factors. To address these limitations, this study proposes an interpretable machine learning framework for predicting the residual drift ratio of reinforced concrete bridge piers. A comprehensive database containing 261 quasi-static experimental datasets was established, incorporating key structural and material parameters, including axial com-pression ratio, shear span ratio, stirrup ratio, longitudinal reinforcement ratio, material strengths, and geometric dimensions. Based on this database, six representative machine learning models were developed and systematically compared. Their predictive performance, robustness, and generalization capability were evaluated using multiple statistical metrics and Monte Carlo simulations. The results show that the CatBoost model consistently outperformed the other models, achieving an R2 value of 0.9629 on the test set while maintaining excellent stability under random data partitions. Furthermore, SHAP analysis was employed to interpret the trained model and quantify the contributions of individual input variables. Eight key factors influencing the residual drift ratio were identified, with the loading displacement ratio (θ) exhibiting the greatest influence. These findings demonstrate that the proposed framework provides an accurate, reliable, and interpretable tool for predicting the post-earthquake residual drift ratio of reinforced concrete bridge piers, offering valuable support for performance-based seismic design, post-earthquake damage assessment, and resilience-based bridge engineering.
Min Zhang, Xuefeng Zhang, Liang-Jun Li et al.· Buildings· 0 citations
With the continuous development of drilling technology, accurately predicting mechanical penetration rates is particularly important for improving operational efficiency and reducing costs. Existing methods often struggle to provide reliable predictions when faced with complex geological conditions and variable drilling environments because they primarily rely on traditional models and fail to adequately consider various influencing factors and their nonlinear relationships. To ad-dress these issues, this paper proposes a mechanical penetration rate prediction model based on committee machines. This model effectively captures the variability characteristics of mechanical penetration rates by integrating multiple expert models while employing wavelet filtering methods to denoise the data to enhance data quality. In the application case, this paper collects relevant drilling parameter data based on a vertical well in a specific block. The evaluation of the model shows that it performs excellently in key indicators such as mean square error, coefficient of determination, root mean square error, and mean absolute error, particularly demonstrating a high predictive capability and stability by explaining 97.19% of data variability. The advantage of the constructed model lies in its strong ensemble learning ability, which not only enhances the prediction accuracy of mechanical penetration rates but also helps to deepen the understanding of the dynamic changes in the drilling process, providing effective support for subsequent drilling optimization and resource development.
Tao Cai, Huai-Yan Qi, Xue-Wu Yang et al.· Journal of Physics, Conferen...· 0 citations
The proposed AFELA–machine learning framework provides a computationally efficient, reliable, and interpretable approach for tunnel stability assessment in sloping rock mass conditions.
A. Kumar, V. Chauhan, Aayush Kumar et al.· Transportation Infrastructur...· 1 citation
Hydraulic fracturing is critical process in unconventional reservoir development, where accurate real time monitoring directly impacts operational safety, job efficiency and financial aspect. This study presents a hybrid framework integrating domain specific operational thresholds with supervised machine learning for anomaly detection and multi parameter prediction. In anomaly detection, XGBoost achieved the highest accuracy of 0.99 and F1-score of 0.98, outperforming random forest (0.97), logistic regression (0.65), KNN (0.90) across traditional evaluation metrics. In predictive model, gradient boosting algorithm achieved the highest R2 score of 0.97 and the lowest RMSE, outperforming both linear and ridge regression model used for base model. The inclusion of operational constraints into the machine learning model enhanced reliability, prediction accuracy, robustness ensuring practical and actionable results for hydraulic fracturing processes.
Improving drilling efficiency remains a central objective in petroleum operations due to its direct impact on time and cost. This study develops a predictive framework for estimating the Rate of Penetration (ROP) using supervised machine learning techniques combined with systematic data conditioning. The analysis is based on more than 11,000 measurements obtained from four horizontal wells. A rigorous preprocessing strategy was implemented to enhance data reliability, including removal of invalid entries and statistical outliers using the interquartile range method. This procedure reduced the dataset to 7,297 high-quality observations. In addition, target stabilization was introduced through Exponential Moving Average smoothing (spans of 5 and 10), which reduced short-term fluctuations and improved the learnability of the ROP signal. Three tree-based regression models—Decision Tree, Random Forest, and Gradient Boosting—were evaluated under both default configurations and optimized settings. Results show that model performance is strongly influenced by data conditioning. The Random Forest model achieved the highest accuracy, with a coefficient of determination (R2) of 0.96 and a mean squared error (MSE) of 26 when trained on the EMA-10 dataset. Gradient Boosting exhibited the largest improvement from hyperparameter tuning, with R2 increasing from 0.86 to 0.95. To bridge the gap between model development and practical use, the trained models were implemented in interactive applications for real-time prediction and parameter optimization. The outcomes demonstrate that careful preprocessing and noise-aware modeling significantly enhance predictive capability.
B. Elahifar, Thomas Philip Fagerli· Journal of Energy Resources...· 0 citations
