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Exploring the Potential of Machine Learning in Predicting Soil California Bearing Ratio Values

PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2025年第2期69卷 551-566页

作者： Wu, Xu Lu, Feng He, Tao Nantong Inst Technol Dept BIM Res Nantong 226002 Peoples R China Nantong Huarong Construct Grp Co Ltd Nantong Jiangsu Peoples R China

Accurately predicting the California Bearing Ratio (CBR) of soil is vital for civil engineering projects as it determines soil strength and stability, crucial for designing safe and durable infrastructure. Conventional methods for calculating CBR values are both expensive and time-consuming, prompting the need for more efficient approaches. This study explores the use of advanced machine learning (ML) techniques to improve workflow and productivity in CBR prediction. Specifically, the improved arithmetic optimization algorithm (IAOA) and the Bonobo optimization algorithm (SBOA) are applied to enhance the Stochastic Gradient Boosting Regression (SGBR) model for predicting CBR values. The SGBR model, known for its ability to handle complex datasets and nonlinear interactions, is optimized to improve predictive accuracy. Performance metrics such as the coefficient of determination (R2), n10-index, and Root Mean Squared Error (RMSE) are used to assess the model's performance. After training, testing, and validation with relevant data, the optimized SGIA model (SGBR enhanced by IAOA) achieves impressive results, including an n10-index of 1.000, a root mean square error of 0.161, and an R2 value of 0.981. These metrics demonstrate the SGIA model's capability to accurately forecast CBR values, offering a reliable, cost-effective alternative to traditional methods for soil evaluation in engineering applications.

关键词： regression analysis California bearing ratio stochastic gradient boosting regression self-adaptive bonobo optimization algorithm improved arithmetic optimization algorithm

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Enhancing stochastic planning in autonomous hybrid energy systems through an advanced arithmetic optimization algorithm and K-means data clustering

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ENERGY REPORTS 2025年 13卷 4375-4387页

作者： Alanazi, Mohana Abdelaziz, Almoataz Y. Hong, Junhee Geem, Zong Woo Jouf Univ Coll Engn Dept Elect Engn Sakaka 72388 Saudi Arabia Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Gachon Univ Dept Smart City & Energy Seongnam 13120 South Korea

In this paper, a stochastic framework for optimal sizing of a stand-alone hybrid energy system, including photovoltaic and wind turbine resources integrated with battery storage, is presented to meet an annual load while incorporating the uncertainties of resource production and load for 20 years of the project life. Monte Carlo simulation and K-means data clustering have been employed for uncertainty modeling and scenario reduction. The decision variables, such as system component sizes, are optimized using an improved arithmetic optimization algorithm (IAOA) to minimize the net present cost (NPC) and consider the reliability constraint as the probability of load supply inability (PLSI). The IAOA enhances the conventional arithmetic optimization algorithm (AOA) using Rosenbrock's direct rotational method to overcome premature convergence. Three configurations of the HES are evaluated: (Case I) a photovoltaic-battery system, (Case II) a wind turbine-battery system, and (Case III) a hybrid photovoltaic-wind turbine-battery system. Results show that Case III, with contributions from all renewable units and reserve energy management, provides the load with lower NPC and higher reliability than Case I and Case II. Additionally, the IAOA delivers the most effective solution, yielding the lowest NPC and PLSI compared to the conventional AOA. The stochastic sizing results indicate that incorporating uncertainty increases NPC and weakens reliability. Specifically, in stochastic sizing for Case III, the PLSI, cost of energy, and NPC were higher than the deterministic approach by 3.96 %, 5.17 %, and 9.15 %, respectively. The results demonstrate that the proposed stochastic sizing framework enhances the decision-making process for energy operators under uncertain conditions, providing insight into the system's costs and reliability.

关键词： Hybrid energy system Stochastic sizing Probability of load supply inability Uncertainty K-means data clustering improved arithmetic optimization algorithm

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Energy Management in Microgrids with Uncertainty in EV and Renewable Sources

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2025年第2期49卷 639-661页

作者： Kumar, Mohit Sharma, Deepesh DCRUST Univ Murthal India

Microgrids are usually referred to as small-scale producer that generates their power from renewable energy sources and distribute with maintaining high quality and fewer losses. Energy management is a critical aspect of microgrid operation, and different techniques can be applied to minimize operational costs and maximize the use of available sources. In this paper, we have developed an energy management system that includes a demand response and day ahead strategy to manage the load demand and power generation of renewable sources with the help of a deep learning method (T-LSTM) to reduce operational costs. In addition, a novel improved arithmetic optimization algorithm technique is applied to further optimize the system. In the demand response strategy, the microgrid operator involves consumers to reduce their electricity usage during periods of high demand or when electricity prices are high, either through direct communication or automated systems. The outcome of this study shows that the improved arithmetic optimization algorithm technique is effective in reducing operational costs by up to 13%. The findings of this research can assist in the development of efficient and cost-effective energy management systems for microgrids, which can help to improve the overall stability and sustainability of the energy infrastructure.

关键词： Energy management system Microgrid T-LSTM improved arithmetic optimization algorithm Demand response

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Short-TermWind Power Forecast Based on STL-IAOA-iTransformer algorithm:A Case Study in Northwest China

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Energy Engineering 2025年第2期122卷 405-430页

作者： Zhaowei Yang Bo Yang Wenqi Liu Miwei Li Jiarong Wang Lin Jiang Yiyan Sang Zhenning Pan Faculty of Science Kunming University of Science and TechnologyKunming650500China Faculty of Electric Power Engineering Kunming University of Science and TechnologyKunming650500China Department of Electrical Engineering and Electronics University of LiverpoolLiverpoolL693GJUK College of Electrical Engineering Shanghai University of Electric PowerShanghai200090China School of Electric Power Engineering South China University of TechnologyGuangzhou510640China

Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart *** numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power *** improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant *** extracted components as well as the weather data are then input into iTransformer for short-term wind power *** final predicted short-term wind power curve is obtained by combining the predicted *** improve the model accuracy,IAOA is employed to optimize the hyperparameters of *** proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been ***,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for *** results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.

关键词： Short-termwind power forecast improved arithmetic optimization algorithm iTransformer algorithm SimuNPS

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Utilization Of Machine-Learning-Based Model Hybridized With Meta-Heuristic Frameworks For Estimation Of Unconfined Compressive Strength

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2025年第8期28卷 1779-1794页

作者： Wang, She Zhang, Qi Wuhan City Polytech Sch Comp & Elect Informat Engn Wuhan 430000 Hubei Peoples R China Wuhan Instrument & Elect Tech Sch Dept Commerce & Trade Wuhan 430205 Hubei Peoples R China

Unconfined compressive strength (UCS) is one of the rocks' most valuable mechanical properties in constructing an accurate geo-mechanical model. It has traditionally been determined through laboratory core sample testing or by analysis of well-log data. After a great deal of effort and growing investment in time, the proper adoption of machine learning methods, especially the radial basis function (RBF), opens a route to promising alternatives against empirical methods for better real-time prediction of UCS. The current study considers the RBF-based machine learning model, whose parameters have been optimized using two enhanced meta-heuristic frameworks: improved arithmetic optimization algorithm (IAOA) and Flying Foxes optimization (FFO). Based on an extensive dataset already used in previous studies and applying some soft computing techniques, vigorous performance metrics such as RMSE, R-2, MAE, U95, and MNB were used to test the developed frameworks. The outcomes indicate a significant outperformance of the hybrid RBFF technique over the solo RBF and RBF-IA frameworks. Specifically, the RBFF model resulted in an R-2 of 0.998, an RMSE of 1.313, and an MNB of -0.003, reflecting its better performance in UCS prediction. This study indicates the efficiency of integrating RBF with meta-heuristic optimization to enhance UCS predictions in geotechnical studies.

关键词： Unconfined compressive strength Radial Basis Function improved arithmetic optimization algorithm Flying Foxes optimization

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The Utilization of a Multi-Layer Perceptron Model for Estimation of the Heating Load

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第6期15卷 116-127页

作者： Chen, Ken Zhu, Wenyao Zhejiang Rongqe Technol Co Ltd Lishui 323000 Zhejiang Peoples R China Lishui Univ Dept Comp Sci & Technol Lishui 323000 Zhejiang Peoples R China

The growing significance of energy-efficient building management techniques has led to research that combines precise heating demand predictions with sophisticated optimization algorithms. This research seeks a comprehensive solution to enhance building energy efficiency, addressing the growing concern for sustainability and responsible resource use in contemporary research and practice. In this research endeavor, the complex topic of energy optimization within the complex domain of heating, ventilation, and air conditioning (HVAC) systems is being tackled with a combination of creative problem- solving techniques and thorough examination. The significance of accurate heating load forecasts for raising HVAC system efficiency and cutting expenses is emphasized in this study. It introduces innovative methods by combining two advanced optimization algorithms, the Artificial Hummingbird algorithm (AHA) and the improved arithmetic optimization algorithm (IAOA), with the Multi-Layer Perceptron (MLP) model. The main objective is to improve heating load forecast accuracy and expedite HVAC system optimization procedures. This study emphasizes how important precise heating load forecasts are to attaining energy efficiency, cost savings, and the ultimate objective of encouraging environmental sustainability in building management. The assessments unequivocally illustrate that the MLAH (Multi-Layer Perceptron with Artificial Hummingbird algorithm) model in the second layer emerges as the most exceptional predictor. It attains an impressive maximum Coefficient of Determination (R2) 2 ) value of 0.998 during the testing phase, reflecting a remarkable explanatory capacity and displaying remarkably low Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values of 0.43 and 0.337, indicating minimal prediction discrepancies compared to alternative models.

关键词： Heating energy consumption heating load Multi- Layer Perceptron Artificial Hummingbird algorithm improved arithmetic optimization algorithm

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Occlusion-aware FERNet: an optimized patch-based adaptive residual network with attention mechanism for occlusion-aware facial expression recognition

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SOFT COMPUTING 2023年第22期27卷 16401-16427页

作者： Prasad, A. Reddy Rajesh, A. Vels Inst Sci Technol & Adv Studies Comp Sci & Engn Chennai India

The traditional FER techniques have provided higher recognition accuracy during FER, but the utilization of memory storage size of the model is high, which may degrade the performance of the FER. In order to address these challenges, an adaptive occlusion-aware FER technique is introduced. The occlusion is significantly detected by employing convolutional neural network (CNN). The face inpainting is accomplished via generative adversarial network (GAN). Lastly, the facial expressions are determined using the patch-based adaptive residual network with attention mechanism (PAResAM), where some hyperparameters are tuned optimally by using an improved arithmetic optimization algorithm (IAOA). The performance is evaluated and measured with diverse metrics. Thus, the outcome of the model ensures that it exploits a higher detection accuracy value.

关键词： Occlusion-aware facial expression recognition Facial images Face inpainting Patch-based adaptive residual network with attention mechanism improved arithmetic optimization algorithm

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Realizing the Improvement of the Reliability and Efficiency of Intelligent Electricity Inspection: IAOA-BP algorithm for Anomaly Detection

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ENERGIES 2023年第7期16卷 3021页

作者： Zou, Yuping Wu, Rui Tian, Xuesong Li, Hua State Grid Tianjin Mkt Serv Ctr Tianjin 300200 Peoples R China Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equipm Tianjin 300401 Peoples R China Hebei Univ Technol Key Lab Electromagnet Field & Elect Apparat Reliab Tianjin 300401 Peoples R China

Anomaly detection can improve the service level of the grid, effectively save human resources and reduce the operating cost of a power company. In this study, an improved arithmetic optimization-backpropagation (IAOA-BP) neural algorithm for an anomaly detection model was proposed for electricity inspection. The dynamic boundary strategy of the cosine control factor and the differential evolution operator are introduced into the arithmetic optimization algorithm (AOA) to obtain the improved arithmetic optimization algorithm (IAOA). The algorithm performance test proves that the IAOA has better solving ability and stability compared with the AOA, WOA, SCA, SOA and SSA. The IAOA was subsequently used to obtain the optimal weights and thresholds for BP. In the experimental phase, the proposed model is validated with electricity data provided by a power company. The results reveal that the overall determination accuracy using the IAOA-BP algorithm remains above 96%, and compared with other algorithms, the IAOA-BP has a higher accuracy and can meet the requirements grid supervision. The power load data anomaly detection model proposed in this study has some implications that might suggest how power companies can promote grid business model transformation, improve economic efficiency, enhance management and improve service quality.

关键词： electricity inspection anomaly detection improved arithmetic optimization algorithm backpropagation neural network

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