Simulation‐Based and Machine Learning Methodologies for Energy Optimization in Buildings

Stuck pipe incidents are one of the contributors to non-productive time (NPT), where they can result in a higher well cost. This research investigates the feasibility of applying machine learning to predict events of stuck pipes during drilling operations in petroleum fields. The predictive model aims to predict the occurrence of stuck pipes so that relevant drilling operation personnel are warned to enact a mitigation plan to prevent stuck pipes. Two machine learning methodologies were studied in this research, namely, the artificial neural network (ANN) and support vector machine (SVM). A total of 268 data sets were successfully collected through data extraction for the well drilling operation. The data also consist of the parameters with which the stuck pipes occurred during the drilling operations. These drilling parameters include information such as the properties of the drilling fluid, bottom-hole assembly (BHA) specification, state of the bore-hole and operating conditions. The R programming software was used to construct both the ANN and SVM machine learning models. The prediction performance of the machine learning models was evaluated in terms of accuracy, sensitivity and specificity. Sensitivity analysis was conducted on these two machine learning models. For the ANN, two activation functions—namely, the logistic activation function and hyperbolic tangent activation function—were tested. Additionally, all the possible combinations of network structures, from [19, 1, 1, 1, 1] to [19, 10, 10, 10, 1], were tested for each activation function. For the SVM, three kernel functions—namely, linear, Radial Basis Function (RBF) and polynomial—were tested. Apart from that, SVM hyper-parameters such as the regularization factor (C), sigma (σ) and degree (D) were used in sensitivity analysis as well. The results from the sensitivity analysis demonstrate that the best ANN model managed to achieve an 88.89% accuracy, 91.89% sensitivity and 86.36% specificity, whereas the best SVM model managed to achieve an 83.95% accuracy, 86.49% sensitivity and 81.82% specificity. Upon comparison, the ANN model is the better machine learning model in this study because its accuracy, sensitivity and specificity are consistently higher than those of the best SVM model. In conclusion, judging from the promising prediction accurateness as demonstrated in the results of this study, it is suggested that stuck pipe prediction using machine learning is indeed practical.

Understanding and predicting complex environmental and hydrosystem processes is a central challenge in Earth system science. These systems are governed by interacting physical mechanisms across scales, are only partially observed, and are often characterized by limited data and substantial uncertainty. As a result, machine learning (ML) has emerged along two complementary development paths for environmental modeling.In a first branch, physical process models remain the backbone of simulation, while ML is employed as a surrogate to approximate expensive numerical solvers. Surrogate modeling approaches based on Gaussian process emulators, polynomial chaos expansions, support vector regression, and related probabilistic representations are particularly well suited for data-poor settings. Neural networks are used more selectively in this context, as uncertainty-aware and sample-efficient methods are often preferred. In surrogate modeling, considerable effort is devoted to optimal sampling strategies, including active learning, which adaptively select informative simulations and help preserve scarce computational resources. These surrogate models enable efficient uncertainty quantification, sensitivity analysis, and Bayesian inference, while preserving physical interpretability.A second, increasingly important branch emerges when physical models are incomplete, unavailable, or deliberately omitted, and ML models replace the governing equations altogether. This branch is most commonly based on neural network representations, but has recently also been explored using Gaussian processes and polynomial chaos–based learning concepts. In this setting, purely data-driven learning is insufficient, as unconstrained models tend to violate physical principles and extrapolate poorly. In this second branch, physical principles such as conservation laws or balance relations are embedded directly into learning architectures. Complementarily, constraint-driven learning strategies enforce physical laws, admissibility conditions, and structural consistency during training. By restricting the hypothesis space, these methods stabilize learning and support robust inference under incomplete physical knowledge.Taken together, surrogate modeling for physics-based simulations and physics-aware ML for equation-free learning represent two coherent and complementary branches of modern environmental machine learning. We observe a growing convergence between these two branches, as physics-based surrogate modeling and equation-free machine learning increasingly borrow concepts from each other. This convergence is not accidental, but a direct response to fundamental model limitations and the challenge of making reliable predictions under scarce data and knowledge constraints. By integrating physics, probabilistic reasoning and constraints, emerging approaches increasingly focus on robustness and interpretability rather than unconstrained expressive power.

In recent times, mechanical and production industries are facing increasing challenges related to the shift towards sustainable manufacturing. In this work, the machining was performed in dry cutting condition with the newly developed AlTiSiN-coated carbide inserts coated through scalable pulsed power plasma technique, and a dataset was generated for different machining parameters and output responses. The machining parameters are speed, feed and depth of cut, while the output responses are surface roughness, cutting force, crater wear length, crater wear width and flank wear. Abrasion and adhesion were found to be the two dominant wear mechanisms. With speed, the tool wear was found to increase. Cutting force was found to increase rapidly for higher speed ranges (70, 80 and 90[Formula: see text]m/min). But reduction of cutting force was observed in both low and medium ranges of cutting speed (40, 50, 55 and 60[Formula: see text]m/min). With higher values of feed and depth of cut, higher cutting force was also noticed. With the variation of depth of cut, the machined surface morphology and machined surface roughness were found to deteriorate. With higher feed and depth of cut values, more surface damages were observed compared to low values of feed and depth of cut. Both the feed rate and tool–chip contact length contributed significantly to the formation of crater on the tool rake surface. At high speed, continuous chips were observed. Both chip sliding and sticking were observed on the tool rake face. The data collected from the machining operation was used for the development of machine learning (ML)-based surrogate models to test, evaluate and optimize various input machining parameters. Different ML approaches such as polynomial regression (PR), random forests (RF) regression, gradient boosted (GB) trees and adaptive boosting (AB)-based regression were used to model different output responses in the hard machining of AISI D6 steel. Out of the four ML methodologies, RF and PR performed better in comparison to the other two algorithms in terms of the [Formula: see text] values of predictions. The surrogate models for different output responses were used to prepare a complex objective function for the germinal center algorithm-based optimization of the machining parameters of the hard turning operation.

Machine learning application in the oil and gas industry is rapidly becoming popular and in recent years has been applied in the optimization of production for various reservoirs. The objective of this paper is to evaluate the efficacy of advanced machine learning algorithms in reservoir production optimization. A 3-D geological model was constructed based on permeability calculated using a machine learning technique which involved different architectures of algorithms tested using a 5-fold cross-validation to decide the best machine learning algorithm. Sensitivity analysis and a subsequent history matching were conducted using a machine learning workflow. The aquifer properties, permeability heterogeneity in different directions and relative permeability were the control variables assessed. Field development scenarios were exploited with the objective to optimize cumulative oil recovery. The impact of using a normal depletion plan to a secondary recovery plan using waterflooding was investigated. Different injection well placement locations, well patterns as well as the possibility of converting existing oil producing wells to water injection wells were exploited. Considering the outcome of an economic analysis, the optimum development strategy was realized as an outcome for the optimization process. Prior to forecasting cumulative oil production using artificial neural network (ANN) for the optimization process on the generated surrogate model, a sensitivity analysis was performed where the well location, injection rates and bottomhole pressure of both the producer and injector wells were specified as control variables. The water cut as part of the optimization process was utilized as a secondary constraint. Forecasting was performed for a 15-year period. The history-matching results from the constructed geological model showed that the oil rate, water rate, bottom hole pressure, and average reservoir pressure were matched within a 10% deviation from the observed data. In this study, the ANN optimizer was found to provide the best results for the field cumulative oil production. Using a secondary recovery development plan was observed to significantly increase the cumulative oil production. A machine learning based proxy model was built for the prediction of cumulative oil production to reduce computational time. In this study, we propose an approach applied to reservoir production optimization utilizing a machine learning workflow. This was accomplished by utilizing a surrogate model which was calibrated with a number of training simulations and then optimized using advanced machine learning algorithms. A detailed economic analysis was also conducted showing the impact of a variety of field development strategies.

Integrative economic framework incorporating the Emission Trading Scheme (ETS) for U.S. Residential energy systems

Investigating adaptive sampling strategies for optimal building energy performance using artificial neural networks and kriging surrogate models

Building performance simulations (BPS) are essential for managing rising energy demands but are computationally intensive, limiting their use in early-stage design. Surrogate models (SMs) offer fast approximations using machine learning but lack generalizability for changing building morphologies. This study introduces three morphology-based feature extraction methods – Zernike image moments, polar raycasting, and principal component analysis – to train a generalizable SM for predicting annual energy use intensity (EUI) of 2.5D residential buildings in Singapore. The best SM achieved a 9.2% RMSPE, with features yielding low error and avoiding overfitting. This framework enables efficient design space exploration, aiding architects and urban designers in optimizing masterplans for energy efficiency. In the final section, we demonstrate a design space exploration (DSE) framework with our generalizable SM using a novel parametric model, showcasing its speed, adaptability and convenience over conventional optimization workflows. Highlights Novel approach explores building morphology's impact to annual EUI . Using Zernike moments, ray casting and PCA to extract features from any 2.5D buildings. 4000 tuned SMs trained from hyperparameter optimisation. Most robust SM achieved 9.2% RMSPE when tested with 800 samples. SM's generalizability is proven by applying it to a design model with previously unseen parameters.

Due to notable part of buildings in the global energy consumption, it is of significant importance to design various scenarios to optimize building energy consumption. Smart windows including electrochromic types are some recently developed ideas that may help through this way controlling the solar radiation. Here a residential building is studied to analyze the performance of electrochromic smart windows in compare with simple single glazing and also triple glazing windows in the cities of Tehran and Yazd, as two choices of hot and dry climate. Results indicate that in the city of Tehran, when comparing with simple single glazing windows, in the case of use of electrochromic windows, 20% and 10% saving is observable in gas and electricity consumption, respectively. These are 17.1% and 7.2% when implementing triple glazing windows. In the city of Yazd also this trend has been experienced with 22% and 12.6% saving in natural gas and electricity in the case of electrochromic smart windows, respectively and that of 17.5% and 8.1% for triple glazing windows, respectively. So implementing this smart window type has led to the building energy optimization by the mean of controlling the amount of solar radiation allowed in the building.

Optimizing the insulation thickness of walls of existing buildings with CHP-based district heating systems

The methodology for seismic performance evaluation of a residential building in Indonesia with the use of seismic isolation is considered. An 8-storey reinforced concrete frame residential building with shear wall structural system was selected as a case study. Nonlinear methods of seismic response analysis were used to calculate the response of the structure: nonlinear static (Pushover) and Nonlinear-Time History Analysis, NLTHA. The analysis is performed in STERA 3D freeware. The nonlinear time history analysis was performed for seven pairs of horizontal components of earthquake ground motions, selected according to the parameters of possible earthquakes for the considered site (Bandung city). The selected earthquake records were modified using the spectral matching procedure for design spectrum. Friction-pendulum bearings developed by Nippon Steel Corporation of Japan were used as seismic isolation. The results of nonlinear time history analysis show that shallow earthquakes result in greater damage compared to megathrust earthquakes, with both scenarios providing a life safety (LS) performance level. The use of seismic isolation can reduce seismic loads, as evidenced by the reduction in top-level accelerations and shear forces at the base.

Computational Fluid Dynamics (CFD) is regarded as an important tool for analyzing the flow field, thermal environment, and air quality around the built environment. However, for built environment applications, the high computational cost of CFD hinders large-scale scenario simulation and efficient design optimization. In the field of built environment research, surrogate modeling has become a key technology to connect the needs of high-fidelity CFD simulation and rapid prediction, whereas the low-dimensional nature of traditional surrogate models is unable to match the physical complexity and prediction needs of built flow fields. Therefore, combining machine learning (ML) with CFD to predict flow fields in built environments offers a promising way to increase simulation speed while maintaining reasonable accuracy. This review briefly reviews traditional surrogate models and focuses on ML-based surrogate models, especially the specific application of neural network architectures in rapidly predicting flow fields in the built environment. The review indicates that ML accelerates the three core aspects of CFD, namely mesh preprocessing, numerical solving, and post-processing visualization, in order to achieve efficient coupled CFD simulation. Although ML surrogate models still face challenges such as data availability, multi-physics field coupling, and generalization capability, the emergence of physical information-driven data enhancement techniques effectively alleviates the above problems. Meanwhile, the integration of traditional methods with ML can further enhance the comprehensive performance of surrogate models. Notably, the online ministry of trained ML models using transfer learning strategies deserves further research. These advances will provide an important basis for advancing efficient and accurate operational solutions in sustainable building design and operation.

The machine learning methodology is gaining immense exposure as a potential methodology for solving and modeling manufacturing problems. The present study deals with the application of machine learning approaches in analyzing and predicting the tensile behavior of friction stir welded AA6082. Rotational speed and feed rate are used as input variables; ultimate tensile strength (UTS) is observed as a response parameter. Full factorial designed is used to perform the experiment. Random forest regression, M5P tree regression, and artificial neural network (ANN) are employed to validate the experimental results. These machine learning-based models are adopted to analyzing the absurdity in actual and predicted data. Random forest regression is observed best performing a machine-learning approach in predicting the tensile behavior of FSW joints. In addition, sensitivity analysis is also carried out to determine the most sensitive factor for UTS. It is observed that rotational speed is the most influencing factor for UTS.

Machine learning (ML) has become an increasingly popular tool for use in neurosurgical research. The number of publications and interest in the field have recently seen significant expansion in both quantity and complexity. However, this also places a commensurate burden on the general neurosurgical readership to appraise this literature and decide if these algorithms can be effectively translated into practice. To this end, the authors sought to review the burgeoning neurosurgical ML literature and to develop a checklist to help readers critically review and digest this work. The authors performed a literature search of recent ML papers in the PubMed database with the terms "neurosurgery" AND "machine learning," with additional modifiers "trauma," "cancer," "pediatric," and "spine" also used to ensure a diverse selection of relevant papers within the field. Papers were reviewed for their ML methodology, including the formulation of the clinical problem, data acquisition, data preprocessing, model development, model validation, model performance, and model deployment. The resulting checklist consists of 14 key questions for critically appraising ML models and development techniques; these are organized according to their timing along the standard ML workflow. In addition, the authors provide an overview of the ML development process, as well as a review of key terms, models, and concepts referenced in the literature. ML is poised to become an increasingly important part of neurosurgical research and clinical care. The authors hope that dissemination of education on ML techniques will help neurosurgeons to critically review new research better and more effectively integrate this technology into their practices.

The cybersecurity concerns get increasingly intricate as the digital world progresses. In light of the increasing complexity of cyber threats, it is imperative to develop and implement advanced and flexible security strategies. Machine Learning (ML) has become a potent tool in strengthening cybersecurity, providing the capacity to scrutinise extensive information, recognise trends, and improve threat detection and defence methods. This paper examines the significance of ML in the field of cybersecurity, with a special emphasis on the identification of threats and the implementation of protective measures. By incorporating ML algorithms into cybersecurity frameworks, organisations may automate decision-making processes, facilitating prompt responses to ever-changing threats. The initial segment explores the terrain of cyber threats, highlighting the necessity for dynamic and aggressive security methods. Conventional solutions that rely on signatures are frequently inadequate when it comes to handling sophisticated, shape-shifting attacks. ML algorithms, in contrast, have exceptional proficiency in identifying nuanced patterns and irregularities within extensive datasets, therefore offering a more efficient method of detecting potential threats. The second section delves into several ML methodologies utilised in cybersecurity, including supervised and unsupervised learning, deep learning, and reinforcement learning. Every approach is assessed based on its suitability for threat detection, demonstrating its advantages and constraints. Furthermore, the relevance of feature engineering and data pretreatment in improving machine learning models for cybersecurity applications. The versatility of ML algorithms allows them to grow with emerging threats, making them a useful tool in the ever-changing arena of cyber warfare. The final segment focuses on real-world applications of machine learning in cybersecurity, presenting successful use cases across sectors. From anomaly detection to behavior analysis, ML algorithms contribute to the discovery of dangerous activity, lowering false positives and strengthening the overall security posture. Lastly, the paper covers the obstacles and ethical issues related to the adoption of ML in cybersecurity. Issues like as adversarial assaults, skewed datasets, and the interpretability of ML models are examined, highlighting the necessity for a holistic strategy that integrates modern technology with ethical considerations. The fusion of human expertise and machine intelligence offers a formidable defense against evolving cyber threats, paving the way for a more resilient and secure digital future.

The exponential growth of biomedical data coupled with advances in machine learning (ML) has created opportunities for more precise diagnosis, enhanced treatment planning, and improved patient management. However, the successful implementation of ML in clinical settings depends on healthcare professionals’ understanding and competency in these technologies. This study examines the effectiveness of integrating ML methodologies into the curricula of Astana Medical University and S. D. Asfendiyarov Kazakh National Medical University. Focusing on childhood allergic conditions such as asthma, rhinitis, and skin diseases, a supervised ML approach (linear regression) was employed to analyze both clinical and educational data. Results showed that the experimental group of students who received ML-integrated training demonstrated significant improvements in analytical competence and data processing accuracy compared to the control group. The ML model achieved a coefficient of determination (R 2 ) of 0,85 with low prediction errors (MAE=0,45, MSE=0,30, RMSE=0,55). Statistical tests supported the hypothesis that structured ML education enhances medical students’ competencies, suggesting that future healthcare professionals trained in ML can better leverage data-driven decision-­making for improved patient care. This study contributes to the growing body of literature advocating for ML integration in medical education and underscores the need for further research into advanced ML algorithms and long-term clinical outcomes.