Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning

Prediction of thermo-mechanical properties of rubber-modified recycled aggregate concrete

A new robust predictive model for lost circulation rate using convolutional neural network: A case study from Marun Oilfield

Accurate estimation of reservoir parameters such as fluid saturations and porosity is important for assessing petroleum volumes, economics and decisionmaking. Such parameters are derived from interpretation of petrophysical logs or time-consuming, expensive core analyses. Not all wells are cored in a field, and the number of fully cored wells is limited. In this study, a time-efficient and economical method to estimate porosity, water saturation and hydrocarbon saturation is employed. Two Least Squares Support Vector Machine (LSSVM) machine learning models, optimized with Particle Swarm Optimization (PSO), were developed to predict these reservoir parameters, respectively. The models were developed based on data from five wells in the Varg field, Central North Sea, Norway where the data were randomized and split into an unseen fraction (10%) and a fraction used to train the models (90%). In addition to the unseen fraction, a sixth well from the Varg field was used to assess the models. The samples are mainly sandstone with different contents of shale, while fluids water, oil and gas were present. The ‘seen’ data were randomized into calibration, validation and testing sets during the model development. The petrophysical logs in the study were Gamma-ray, Self-potential, Acoustic, Neutron porosity, bulk density, caliper, deep resistivity, and medium resistivity. The log based inputs were made more linear (via log operations) when relevant and normalized to be more comparable in the algorithms. Feature selection was conducted to identify the most relevant petrophysical logs and remove those that are considered less relevant. Three and four of the eight logs were sufficient, to reach optimum performance of porosity and saturation prediction, respectively. Porosity was predicted with R2 = 0.79 and 0.70 on the model development set and unseen set, for saturation it was 0.71 and 0.61, a similar performance as on the training and testing sets at the development stage. The R2 was close to zero on the new well, although the predicted values were physical and within the observed data scatter range as the model development set. Possible improvements were identified in dataset preparation and feature selection to get more robust models.

To achieve accurate prediction of tunnel boring machine (TBM) penetration rate (PR) under complex geological conditions, this study proposes an interpretable machine learning (ML) framework optimized using Bayesian optimization (BO). A dataset comprising 411 samples collected from two TBM projects was established, incorporating key geological parameters (uniaxial compressive strength, elastic modulus, and Poisson’s ratio) and operational parameters (thrust force and rotation speed). Seven ML models, including decision tree, Gaussian process regression, k-nearest neighbors, random forest (RF), support vector regression, extreme gradient boosting, and light gradient boosting machine, were systematically optimized using BO under five-fold cross-validation. The results demonstrate that ensemble learning models exhibit superior predictive performance. Among them, the RF model achieved the highest accuracy on the testing set with an R 2 of 0.9582, followed by the light gradient boosting machine with an R 2 of 0.9565. SHapley Additive exPlanations (SHAP) analysis indicates that uniaxial compressive strength is the dominant factor controlling PR, while thrust force act as the primary controllable parameters. The proposed framework effectively integrates high predictive accuracy with strong interpretability, providing a reliable methodological basis for intelligent TBM performance prediction and construction parameter optimization.

Prediction of UCS and BTS under freeze-thaw conditions in the NW himalayan rock mass using petrographic analysis and laboratory testing

Synthetic shear sonic log generation utilizing hybrid machine learning techniques

In one-dimensional (1D) geomechanical modeling, precise predictions are essential for ensuring wellbore stability, defining safe mud weight windows, and determining hydraulic fracturing pressure ranges. However, accurately estimating geomechanical parameters, especially in data-limited environments, remains a significant challenge. This study addresses these challenges by integrating advanced machine learning algorithms with well log data from two wells in one of Iran’s hydrocarbon fields. The dataset includes shear wave velocity (Vs) logs, static Young’s modulus (Est) samples, uniaxial compressive strength (UCS) samples, internal friction angle ( φ ) measurements, and pore pressure (PP) samples. To tackle the challenge of predicting Vs with high precision, three sophisticated algorithms were employed: Bidirectional Long Short-Term Memory (BiLSTM), BiLSTM+Transformer, and a hybrid model (CBiT) combining 1D Convolutional Neural Network (CNN), BiLSTM, and Transformer mechanisms. Feature selection using Pearson correlation and Mutual Information and SHapley Additive exPlanations (SHAP) analysis identified the most influential parameters for each algorithm. The models were evaluated against traditional empirical methods using statistical metrics, such as mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R2). The results revealed that the machine learning models significantly outperformed the empirical methods, with the hybrid CBiT model showing superior accuracy, robustness, and generalization capability, establishing a new benchmark in predictive performance for shear wave velocity due to its ability to capture complex nonlinear relationships and long-term dependencies. Particle Swarm Optimization (PSO) was applied to further optimize model hyperparameters, achieving R2 values of 0.9989 (training) and 0.9985 (testing), with RMSE for unseen data reduced to 0.0269 and R2 reaching 0.9973. Dynamic elastic parameters, including Young’s modulus, bulk modulus, and shear modulus, were calculated using the optimized model. Strong correlations between dynamic and static properties, such as dynamic Young’s modulus and UCS, were extrapolated across the entire depth range of both wells. The study also introduced a novel approach to estimate internal friction angle using Plumb’s correlation and laboratory data, alongside improved pore pressure predictions based on Eaton’s empirical method. Finally, vertical and horizontal stresses were calculated using poroelastic equations for comprehensive stress modeling. This study demonstrates how the integration of hybrid machine learning algorithms, advanced optimization techniques, and rigorous data analysis can significantly improve the accuracy, reliability, and practical applicability of 1D geomechanical models, providing a robust and efficient framework for subsurface analysis and offering substantial advantages over conventional approaches.

Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

This study focuses on using various machine learning (ML) models to evaluate the shear behaviors of ultra-high-performance concrete (UHPC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The main objective of the study is to predict the shear strength of UHPC beams reinforced with GFRP bars using ML models. We use four different ML models: support vector machine (SVM), artificial neural network (ANN), random forest (R.F.), and extreme gradient boosting (XGBoost). The experimental database used in the study is acquired from various literature sources and comprises 54 test observations with 11 input features. These input features are likely parameters related to the composition, geometry, and properties of the UHPC beams and GFRP bars. To ensure the ML models' generalizability and scalability, random search methods are utilized to tune the hyperparameters of the algorithms. This tuning process helps improve the performance of the models when predicting the shear strength. The study uses the ACI318M-14 and Eurocode 2 standard building codes to predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams. The ML models' predictions are compared to the results obtained from these building code standards. According to the findings, the XGBoost model demonstrates the highest predictive test performance among the investigated ML models. The study employs the SHAP (SHapley Additive exPlanations) analysis to assess the significance of each input parameter in the ML models' predictive capabilities. A Taylor diagram is used to statistically compare the accuracy of the ML models. This study concludes that ML models, particularly XGBoost, can effectively predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams.

Systematic literature review of machine learning based software development effort estimation models

Accurate prediction of underground pillar stability statuses (i.e., stable, unstable or failed) is of great significance for mining safety, but the limited prior data for pillar failure makes it a critical challenge. The present study proposes a novel feature fusion learning (FFL) framework to address this issue by integrating feature fusion, machine learning (ML) models, and category voting. The FFL strategy expands limited datasets through cross-reconstruction of feature vectors, converting multi-class stability prediction into a binary classification task while preserving original feature attributes. Comparative analysis of the proposed FFL strategy with two widely used data augmentation techniques (i.e., SMOTE and GAN), combined with three ML models [Random Forests (RF), LightGBM and XGBoost] demonstrates the superiority of FFL. Results reveal that FFL achieves higher accuracy (0.89), precision (0.88), recall (0.86), and F1-score (0.87) when paired with the XGBoost model, outperforming conventional methods. SHAP analysis underscores pillar stress, uniaxial compressive strength (UCS), and width-to-height ratio as dominant predictors, while feature fusion enriches dimensionality without compromising interpretability. The framework exhibits robustness across datasets with varying feature vectors and sample sizes, validating its adaptability to real-world scenarios with sparse data. This work provides a reliable and interpretable tool for pillar stability assessment, enhancing the safety of deep mining operations.

In this study, six machine learning (ML) models, namely, random forest (RF), Gaussian process regression (GPR), support vector regression (SVR), decision tree (DT), least squares support vector machine (LSSVM), and multivariate adaptive regression spline (MARS) models, were employed to reconstruct the missing daily‐averaged discharge in a mega‐delta from 1980 to 2015 using upstream‐downstream multi‐station data. The performance and accuracy of each ML model were assessed and compared with the stage‐discharge rating curves (RCs) using four statistical indicators, Taylor diagrams, violin plots, scatter plots, time‐series plots, and heatmaps. Model input selection was performed using mutual information and correlation coefficient methods after three data pre‐processing steps: normalization, Fourier series fitting, and first‐order differencing. The results showed that the ML models are superior to their RC counterparts, and MARS and RF are the most reliable algorithms, although MARS achieves marginally better performance than RF. Compared to RC, MARS and RF reduced the root mean square error (RMSE) by 135% and 141% and the mean absolute error by 194% and 179%, respectively, using year‐round data. However, the performance of MARS and RF developed for the climbing (wet season) and recession (dry season) limbs separately worsened slightly compared to that developed using the year‐round data. Specifically, the RMSE of MARS and RF in the falling limb was 856 and 1,040 m3/s, respectively, while that obtained using the year‐round data was 768 and 789 m3/s, respectively. In this study, the DT model is not recommended, while the GPR and SVR models provide acceptable results.

Accurate estimation of reference evapotranspiration (ETo) plays a vital role in irrigation and water resource planning. The Penman–Monteith method recommended by the Food and Agriculture Organization (FAO PM56) is widely used and considered a standard to calculate ETo. However, FAO PM56 cannot be used with limited meteorological variables, so it is compulsory to choose an alternative model for ETo estimation, which requires fewer variables. This study built ten machine learning (ML) models based on multi-function, neural network, and tree-based structure against the FAO PM56 method. For this purpose, time series temperature data on a monthly scale are only used to train ML models. The developed ML models were applied to estimate ETo at different test stations and the obtained results were compared with the FAO PM56 method to verify and validate their performance in ETo estimation for the selected stations. In addition, multiple statistical indicators, including root-mean-square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (r) were calculated to compare the performance of each ML model on ETo estimation. Among the applied ML models, the ETo tree boost (TB) ML model outperformed the other ML models in estimating ETo in diverse climatic conditions based on statistical indicators (R2, NSE, r, RMSE, and MAE). Moreover, the observed R2, NSE, and r were the highest for the TB ML model, while RMSE and MAE were found to be the lowest at the study sites compared to other applied ML models. Lastly, ETo point data yielded from the TB ML model was used in an interpolation process to create monthly and annual ETo maps. Based on the ETo maps, this study suggests mainly a focus on areas with high ETo values and proper irrigation scheduling of crops to ensure water sustainability.

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

This study explores the application of data-driven approaches to enhance the interpretation of geophysical wellbore data for carbon capture and storage (CCS) in the Illinois Basin. Extensive data from exploration and pilot-scale projects are analyzed to assess their potential for generating synthetic datasets that can effectively replicate some missing information for ongoing projects. Specifically, various machine learning (ML) models, including random forest, gradient boosting, feed-forward neural networks, and others, are employed to predict sonic velocities based on the mineralogical composition of participating rock and to identify fracture locations using petrophysical logs. ML-based approaches significantly outperform the traditional effective media interpretation, exhibiting improved accuracy in sonic velocity prediction and capturing small-scale heterogeneity of the formations. However, the prediction of fracture presence remains challenging due to data imbalance and the complex interplay between fractures and inherent heterogeneity of geologic formations. Application of t-distributed stochastic neighbor embedding (t-SNE)—an advanced technique for multidimensional data visualization—reveals that spatial heterogeneity strongly influences geophysical properties, limiting ML models’ performance in detecting fractures. Despite these challenges, ML approaches show promise in complementing traditional methodologies, enabling faster and more informed decision-making processes during early project stages. Future research is needed to address data limitations and enhance the reliability of ML models in diverse geological settings.