Modelling, Volume 6, Issue 2 (June 2025) – 11 articles

This systematic review investigates the integration of artificial intelligence (AI) in cost estimation within project management, focusing on its impact on accuracy and efficiency compared to traditional methods. This study synthesizes findings from 39 high-quality articles published between 2016 and 2024, evaluating various machine learning (ML), deep learning (DL), regression, and hybrid models in sectors such as construction, healthcare, manufacturing, and real estate. The results show that AI-powered approaches, particularly artificial neural networks (ANNs)—which constitute 26.33% of the studies—, enhance predictive accuracy and adaptability to complex, dynamic project environments. Key AI techniques, including support vector machines (SVMs) (7.90% of studies), decision trees, and gradient-boosting models, offer substantial improvements in cost prediction and resource optimization. ML models, including ANNs and deep learning models, represent approximately 70% of the reviewed studies, demonstrating a clear trend toward the adoption of advanced AI techniques. On average, deep learning models perform with 85–90% accuracy in cost estimation, making them highly effective for handling complex, nonlinear relationships and large datasets. Machine learning models achieve an average accuracy of 75–80%, providing strong performance, particularly in industries like road construction and healthcare. Regression models typically deliver 70–80% accuracy, being more suitable for simpler cost estimations where the relationships between variables are linear. Hybrid models combine the strengths of different algorithms, achieving 80–90% accuracy on average, and are particularly effective in complex, multi-faceted projects. Overall, deep learning and hybrid models offer the highest accuracy in cost estimation, while machine learning and regression models still provide reliable results for specific applications. Full article

The optimal design of distillation separation processes has become a fundamental tool in industries in order to minimize operating costs and investments. In many cases, the optimization stage has been carried out using metaheuristics, with the process simulation stage carried out externally to the optimization. This paper presents an optimal design methodology for separating the components of turpentine, a raw material of natural origin, based on coupling a distillation process simulator with the Firefly metaheuristic as an optimizer. Results were obtained for a distillation process to obtain $\alpha$-pinene and $\beta$-pinene (two of the main components of turpentine), meeting purity criteria in the top products of the equipment while minimizing a measure of the total annualized cost. The results show that the tool developed—together with the Firefly algorithm—is capable of obtaining optimized results (although there is no guarantee of a global optimum) from a small set of initial design configurations. Full article

Human Activity Recognition (HAR) has recently attracted the attention of researchers. Human behavior and human intention are driving the intensification of HAR research rapidly. This paper proposes a novel Motion History Mapping (MHI) and Orientation-based Convolutional Neural Network (CNN) framework for action recognition and classification using Machine Learning. The proposed method extracts oriented rectangular patches over the entire human body to represent the human pose in an action sequence. This distribution is represented by a spatially oriented histogram. The frames were trained with a 3D Convolution Neural Network model, thus saving time and increasing the Classification Correction Rate (CCR). The K-Nearest Neighbor (KNN) algorithm is used for the classification of human actions. The uniqueness of our model lies in the combination of Motion History Mapping approach with an Orientation-based 3D CNN, thereby enhancing precision. The proposed method is demonstrated to be effective using four widely used and challenging datasets. A comparison of the proposed method’s performance with current state-of-the-art methods finds that its Classification Correction Rate is higher than that of the existing methods. Our model’s CCRs are 92.91%, 98.88%, 87.97.% and 87.77% which are remarkably higher than the existing techniques for KTH, Weizmann, UT-Tower and YouTube datasets, respectively. Thus, our model significantly outperforms the existing models in the literature. Full article

Passenger flow forecasting is crucial for optimizing urban transit operations, especially in developing countries such as India, where congestion, infrastructure constraints, and diverse commuter behaviors pose significant challenges. Despite its importance, limited research explored forecasting models for Indian urban transit systems, particularly incorporating the effects of holidays and disruptions caused by the COVID-19 pandemic. To address this gap, we propose TBATS Boosting, a novel hybrid forecasting model that integrates the statistical strengths of trigonometric, Box–Cox, ARMA, trend, and seasonal (TBATS) with the predictive power of LightGBM. The model is trained on a five-year real-world dataset from e-ticketing machines (ETM) in Thane Municipal Transport (TMT), incorporating holiday and pandemic-related variations. While Route 12 serves as a primary evaluation route, different station pairs are analyzed to validate their scalability across varying passenger demand levels. To comprehensively evaluate the proposed framework, a rigorous performance assessment was conducted using MAE, RMSE, MAPE, and WMAPE across station pairs characterized by heterogeneous passenger flow patterns. Empirical results demonstrate that the TBATS Boosting approach consistently outperforms benchmark models, including standalone SARIMA, TBATS, XGBoost, and LightGBM. By effectively capturing complex temporal dependencies, multiple seasonalities, and nonlinear relationships, the proposed framework significantly enhances forecasting accuracy. These advancements provide transit authorities with a robust tool for optimizing resource allocation, improving service reliability, and enabling data-driven decision making across varied and dynamic urban transit environments. Full article

Floating photovoltaic (FPV) systems offer a promising renewable energy solution, particularly for coastal waters. This preliminary numerical study proposes a single-array pentamaran configuration designed to maximize panel installation and enhance stability by reducing rolling motion. The study investigates the effect of mooring length on the motion behavior of FPV systems and actual line tension using the Boundary Element Method (BEM) in both frequency and time domains under irregular wave conditions. The results demonstrate that the mooring system significantly reduces all horizontal motion displacements, with reductions exceeding 90%. Even with a reduction of up to 51% in the unstretched mooring length, from the original design (304.53 m) to the shortest alternative (154.53 m), the motion response shows minimal change. This is supported by RMSE values of only 0.01 m/m for surge, 0.02 m/m for sway, and 0.09 deg/m for yaw. In the time-domain response, the shortened mooring line demonstrates improved motion performance. This improvement comes with the consequence of stronger nonlinearity in restoring forces and stiffness, resulting in higher peak tensions of up to 15.79 kN. Despite this increase, all configurations remain within the allowable tension limit of 30.69 kN, indicating that the FPV’s system satisfies safety criteria. Full article

The goal of the present investigation is to explore the heater position and tilting angle of geometry on a buoyant convective stream and energy transport in a tilted, curved porous cavity. This work can be utilized in the field of solar panel construction and electrical equipment cooling. Since no study has explored the impact of the heater location in an inclined wavy porous chamber, three locations of the heater of finite length on the left sidewall, viz., the top, middle, and bottom, are explored. The stream through the porous material is explained by the Darcy model. The upper and lower walls, as well as the remaining area in the left wall, are covered with thermal insulation, while the curved right sidewall maintains the lower temperature. The governing equations and related boundary conditions are discretized by the finite difference approximations. The equations are then iteratively solved for different heater positions, inclinations, Darcy–Rayleigh number (Ra_D), and corrugation of the right walls. It is witnessed that the heater locations and cavity inclinations alter the stream and thermal fields within the curved porous domain. Furthermore, all heating zones benefit from improved heat conduction due to the right sidewall’s waviness and the tilted porous domain. Full article

The integration of Distributed Energy Resources (DERs) into power grids presents significant challenges to grid performance, requiring innovative solutions for effective operation. Dynamic Operating Envelopes (DOEs) offer a promising approach by optimizing the use of existing infrastructure while ensuring compliance with network constraints. This paper reviews various DOE calculation methodologies, focusing on Optimal Power Flow (OPF)-based methods. Key findings include the role of DOEs in optimizing import and export limits, with critical factors such as forecast accuracy, network modelling, and the effects of mutual phase coupling in unbalanced networks identified as influencing DOE performance. The paper also explores the integration of DOEs into smart grid frameworks, examining both centralized and decentralized control strategies, as well as their potential for providing ancillary services. Challenges in scaling DOEs are also discussed, particularly regarding the need for accurate forecasts, computational resources, communication infrastructure, and balancing efficiency and fairness in resource allocation. Lastly, future research directions are proposed, focusing on the practical application of DOEs to improve grid performance and support network operations, as well as the development of more robust DOE calculation methodologies. This review provides a comprehensive overview of current DOE research and identifies avenues for further exploration and advancement. Full article

One of the most well-known problems of dynamics is the “ladder problem”. In this paper, a theoretical model is proposed followed by the experimental validation of the predicted solution. The model refers to a rod of negligible thickness with the ends leaning frictionless on two walls. By approximating the rod as a segment, the problem is simplified, and the Lagrange equations can be applied. The experimental validation of the model had to address several challenges: the actual rod–wall contacts are singular points, friction cannot be neglected, and the rod’s motion must remain confined to the vertical plane. The physical “ladder” was designed as a cylindrical rod with two identical balls of well-controlled geometry, fixed at the ends. These spheres make contact with two half-cylinder grooves—one vertical and one horizontal—ensuring that the motion remains parallel to the vertical plane. The presence of dry friction in the sphere–groove contacts leads to a complex, strongly nonlinear differential equation of motion, requiring numerical methods of integration. A test-rig was designed and constructed for the experimental study of motion, and an aspect overlooked by the theoretical model was emphasised: the interruption of contact with the vertical wall. An excellent agreement was found between the experimental data and the theoretical results. Full article

The timely identification of probable causes in aviation incidents is crucial for averting future tragedies and safeguarding passengers. Typically, investigators rely on flight data recorders; however, delays in data retrieval or damage to the devices can impede progress. In such instances, experts resort to supplementary sources like eyewitness testimonies and radar data to construct analytical narratives. Delays in this process have tangible consequences, as evidenced by the Boeing 737 MAX accidents involving Lion Air and Ethiopian Airlines, where the same design flaw resulted in catastrophic outcomes. To streamline investigations, scholars advocate for natural language processing (NLP) and topic modelling methodologies, which organize pertinent aviation terms for rapid analysis. However, existing techniques lack a direct mechanism for deducing probable causes. To bridge this gap, this study trains and evaluates the performance of a transformer-based model in predicting the likely causes of aviation incidents based on long-input raw text analysis narratives. Unlike traditional models that classify incidents into predefined categories such as human error, weather conditions, or maintenance issues, the trained model infers and generates the likely cause in a human-like narrative, providing a more interpretable and contextually rich explanation. By training the model on comprehensive aviation incident investigation reports like those from the National Transportation Safety Board (NTSB), the proposed approach exhibits promising performance across key evaluation metrics, including BERTScore with Precision: (M = 0.749, SD = 0.109), Recall: (M = 0.772, SD = 0.101), F1-score: (M = 0.758, SD = 0.097), Bilingual Evaluation Understudy (BLEU) with (M = 0.727, SD = 0.33), Latent Semantic Analysis (LSA similarity) with (M = 0.696, SD = 0.152), and Recall Oriented Understudy for Gisting Evaluation (ROUGE) with a precision, recall and F-measure scores of (M = 0.666, SD = 0.217), (M = 0.610, SD = 0.211), (M = 0.618, SD = 0.192) for rouge-1, (M = 0.488, SD = 0.264), (M = 0.448, SD = 0.257), M = 0.452, SD = 0.248) for rouge-2 and (M = 0.602, SD = 0.241), (M = 0.553, SD = 0.235), (M = 0.5560, SD = 0.220) for rouge-L, respectively. This demonstrates its potential to expedite investigations by promptly identifying probable causes from analysis narratives, thus bolstering aviation safety protocols. Full article

The integration of Finite Element Method (FEM) simulations with laser-based techniques has significantly advanced acoustic research by enhancing wave measurement, analysis, and prediction in complex solid media. This review examines the role of the FEM in laser-based acoustics for wave propagation, defect detection, biomedical diagnostics, and engineering applications. FEM models simulate ultrasonic wave generation and propagation in single-layer and multilayered structures, while laser-based experimental techniques provide high-resolution validation, improving modeling accuracy. The synergy between laser-generated ultrasonic waves and FEM simulations enhances defect detection and material integrity assessment, making them invaluable for non-destructive evaluation. In biomedical applications, the FEM aids in tissue characterization and disease detection, while in engineering, its integration with laser-based methods contributes to noise reduction and vibration control. Furthermore, this review provides a comprehensive synthesis of FEM simulations and experimental validation while also highlighting the emerging role of artificial intelligence and machine learning in optimizing FEM models and improving computational efficiency, which has not been addressed in previous studies. Key advancements, challenges, and future research directions in laser-based acoustic applications are discussed. Full article

The rise of telehealth in smart cities has introduced both opportunities and challenges, particularly in securing sensitive patient data and ensuring reliable authentication. This paper presents a chaos-based dynamic authentication scheme designed to address these challenges. Utilizing the inherent unpredictability and sensitivity of chaotic systems, the proposed method ensures robust protection against various attacks, including replay, brute-force, man-in-the-middle, collision, and parameter prediction. The scheme operates through a dynamic challenge–response mechanism using chaotic maps, which generate highly unpredictable authentication parameters. Simulations demonstrate the system’s strong resilience, minimal collision rate, and adaptability to diverse telehealth devices. By safeguarding sensitive telehealth data and promoting secure access control, this research provides a foundational framework for implementing secure authentication systems in smart cities. Future directions include real-world deployment and integration with advanced technologies like blockchain to further enhance security and scalability. Full article