Development of a Hybrid Model for Risk Assessment and Management in Complex Road Infrastructure Projects

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2736 - 2736

Published: March 4, 2025

During the execution of road infrastructure projects, project managers face significant challenges, including financial, technical, regulatory, and operational risks. More than 90% projects have incurred costs exceeding initial estimates, impacting both completion timelines efficiency infrastructure. Effectively assessing managing these risks is crucial for improving outcomes ensuring sustainability investments. To address this study developed a hybrid model risk assessment management in projects. The quantifies across seven key categories: Design, External, Resource, Employer, Contractor, Engineer, Project, based on three primary input factors: Environment coefficient, Contractual Design coefficient. Initially, various machine learning models, linear regression, Random Forest, Gradient Boosting, Stacking Models, neural networks, were applied to assess predictions. However, due specific nature dataset, models did not achieve satisfactory predictive accuracy. As result, fuzzy logic systems (Mamdani Sugeno) employed, demonstrating superior performance modeling occurrence probabilities. Comparative analysis between two approaches revealed that Sugeno provided most accurate findings highlight benefits applying complex providing structured framework enhancing decision-making processes. This provides methodology accurately predicting safety, efficiency, long-term sustainability.

Language: Английский

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Predicting Extension of Time and Increasing Contract Price in Road Infrastructure Projects Using a Sugeno Fuzzy Logic Model

Mathematics, Journal Year: 2024, Volume and Issue: 12(18), P. 2852 - 2852

Published: Sept. 13, 2024

Road infrastructure plays a crucial role in the development of countries, significantly influencing economic growth, social progress, and environmental sustainability. Major projects are frequently challenged by substantial risks uncertainties, leading to delays, budget overruns, compromised quality. These issues can undermine viability efficiency projects, making effective risk management essential for minimizing negative impacts ensuring project success. For these reasons, study was conducted using Sugeno fuzzy logic system applied completed projects. The resulting model is based on 10 characteristics provides highly accurate predictions Extension Time (EoT) Increasing Contract Price (ICP). By utilizing this model, be improved through more forecasting potential delays cost overruns. high precision enables better assessment proactive decision-making, allowing managers implement targeted strategies mitigate optimize outcomes.

Language: Английский

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A Fuzzy Inertia-Based Virtual Synchronous Generator Model for Managing Grid Frequency Under Large-Scale Electric Vehicle Integration

Processes, Journal Year: 2025, Volume and Issue: 13(1), P. 287 - 287

Published: Jan. 20, 2025

The rapid proliferation of EVs has ushered in a transformative era for the power industry, characterized by increased demand volatility and grid frequency instability. In response to these challenges, this paper introduces novel approach that combines fuzzy logic with adaptive inertia control improve stability grids amidst large-scale electric vehicle (EV) integration. proposed methodology not only adapts varying charging scenarios but also strikes balance between steady-state dynamic performance considerations. This research establishes solid theoretical foundation inertia-adaptive virtual synchronous generator (VSG) concept pioneering inertia-based VSG methodology. Additionally, it incorporates output scaling factors enhance robustness adaptability strategy. These contributions offer valuable insights into evolving landscape strategies provide pragmatic solution pressing challenges arising from integration EVs, ultimately fostering resilience sustainability contemporary systems. Finally, simulation results illustrate new method is effective superior advantages over traditional droop strategies. Specifically, reduces maximum change 25% during load transitions, peak variation 0.15 Hz compared 0.2 VSG.

Language: Английский

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A Takagi–Sugeno fuzzy controller for minimizing cancer cells with application to androgen deprivation therapy

Healthcare Analytics, Journal Year: 2023, Volume and Issue: 4, P. 100277 - 100277

Published: Nov. 2, 2023

Androgen deprivation therapy (ADT) is frequently used to treat prostate cancer which a widespread disease having very low survival rate. A prolonged course of ADT can increase toxicity and drug resistance. This study proposes an adaptive combining chemotherapy or immunotherapy with the discontinuation hormone overcome these obstacles. The super-twisting sliding mode control (STSMC) algorithm found be one effective approach as model for obtaining suitable dosage adaptively. primary objective rapidly reduce number cells duration exposure. Takagi–Sugeno fuzzy controller-based active introduced, it's performance compared STSMC algorithm. While maintaining global asymptotic stability, controller reduces six months. controllers are implemented utilizing linear matrix inequality (LMI) yet another LMI (YALMIP) toolset MATLAB, their efficacy validated MATLAB Simulink simulations. presents novel improve treatment outcomes by integrating nonlinear algorithms strategies minimize exposure, thereby improving patient in management.

Language: Английский

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Sugeno Fuzzy Personality Prediction System: An Approach to Overcoming Psychological Measurement Uncertainty

Indonesian Journal of Data and Science, Journal Year: 2024, Volume and Issue: 5(3), P. 216 - 228

Published: Dec. 31, 2024

Personality prediction is a significant field in psychological measurement, yet it faces challenges due to data's ambiguous and uncertain nature. This study aims develop Sugeno-based fuzzy logic system for predicting personality types according the Myers-Briggs Type Indicator (MBTI). The dataset includes synthetic data, incorporating age, introversion, sensing, thinking, judging. fuzzification process converts crisp input values into variables, which are then processed using predefined rules generate predictions. defuzzification step yields outputs corresponding MBTI types, demonstrating system's ability handle uncertainty ambiguity effectively. Implementation evaluation were conducted Python LabVIEW, revealing satisfactory performance with low error rate of 0.445. highlights potential logic, particularly Sugeno method, enhancing accuracy adaptability prediction, contributing applications education, human resource management, personalized digital services.

Language: Английский

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An integrated approach for fuzzy rule generation in dataset classification using hybrid grid partitioning and rough set theory

International Journal of Enterprise Modelling, Journal Year: 2023, Volume and Issue: 17(2), P. 14 - 25

Published: May 30, 2023

This research presents an integrated approach for fuzzy rule generation in dataset classification by combining hybrid grid partitioning and rough set theory. The objective is to enhance the accuracy interpretability of models. leverages achieve localized generation, capturing local characteristics patterns within different regions feature space. Furthermore, theory applied attribute reduction, identifying most relevant features reducing complexity problem. generated rules provide interpretable understandable that facilitate domain expert interpretation. contributes field proposing a comprehensive framework improves both classification. findings demonstrate effectiveness approach, although certain limitations exist. Future should focus on parameter selection, scalability challenges, applicability diverse problem domains. promising methodology enhancing classification, with potential applications various domains where accurate models are crucial.

Language: Английский

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Integrating hybrid grid partition and rough set method for fuzzy rule generation: a novel approach for accurate dataset classification

International Journal of Enterprise Modelling, Journal Year: 2023, Volume and Issue: 17(2), P. 35 - 45

Published: May 30, 2023

Accurate dataset classification is a critical task in various domains, and combining different methodologies can enhance performance. This research presents novel approach that integrates Hybrid Grid Partition Rough Set methods for fuzzy rule generation, aiming to improve accuracy interpretability classification. The proposed leverages discretize continuous attributes attribute reduction identify essential attributes, enabling accurate while handling uncertainty imprecision. generated rules provide interpretability, aiding decision-making processes providing insights into factors. approach's robustness generalization capabilities are demonstrated through experiments on diverse datasets, indicating its potential applicability real-world scenarios. However, limitations such as the absence of specific evaluation metrics need further validation larger datasets acknowledged. Overall, this contributes by offering integrated highlighting areas future investigation refinement

Language: Английский

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A Review of Interval Valued Type 2 Fuzzy Rule-Based Classifiers

Published: July 9, 2023

This paper presents a review of 21 past works on developing interval-valued type-2 fuzzy rule-based classifiers (IVT2FRBC) for various applications. Special attention is paid to two major topics: design and performance evaluation. The first topic involves decisions made pertaining every relevant component IVT2FRBC such as rule structure, number input variables/features, type & membership functions each variable, model construction method, tuning, so on. second includes measures used evaluate classifier's performance, compared, statistical tests carried out describe the differences between/among classifiers. Based review, observations are their soundness usefulness.

Language: Английский

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