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artificial intelligence algorithms, simulation tools and software for optimization of adaptive facades: A systematic literature review

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JOURNAL OF BUILDING ENGINEERING 2025年 106卷

作者： Ozluk, Resul Aydin, Fatih Yildiz, Yusuf Balikesir Univ Dept Architecture Balikesir Turkiye Balikesir Univ Dept Comp Engn Balikesir Turkiye

In an increasingly digital world, many methods such as AI algorithms, simulation tools, and software are used for performance-based optimization of adaptive facades, and rapid developments are taking place in these areas. Currently, although there are many studies on these topics, they are not yet fully understood and addressed holistically. To fill this gap, this paper conducted a comprehensive review of these researches. In order to accomplish this objective, a bibliometric approach has been conducted with studies published between 2000 and 2023 to systematically analyze the literature on these topics. Using the systematic literature review, the case study location, case study building types, AF movement typologies, sustainability aspects, objectives of the studies and design parameters to achieve these objectives are investigated within the study scope. As a result, among the artificial intelligence algorithms used to optimize the performance of adaptive facades and their typologies, GAs (GA, NSGA-2, MOGA, MOEA) were found as the most widely used algorithms. Furthermore, the common software, simulation and modelling tools required in optimization process are Grasshopper, EnergyPlus and RhinoGrasshopper, respectively. Finally, this review paper will make a general database for current and emerging AI algorithms and tools used in optimization of adaptive fa & ccedil;ade.

关键词： Adaptive facades Energy performance User comfort Systematic literature review artificial intelligence algorithms Simulation tools

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artificial intelligence algorithms for Hybrid Electric Powertrain System Control: A Review

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ENERGIES 2025年第8期18卷 2018-2018页

作者： Zhong, Dawei Liu, Bolan Liu, Liang Fan, Wenhao Tang, Jingxian Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

With the accelerating depletion of fossil fuels and growing severity of air pollution, hybrid electric powertrain systems have become a research hotspot in transportation, owing to their ability to improve fuel economy and reduce emissions. However, optimizing the control of these systems is challenging, as it involves multi-power source coordination, dynamic operating condition adaptation, and real-time energy distribution. Traditional control methods, whether rule-based or optimization-based, often lack global optimality and adaptability. In recent years, artificial intelligence algorithms have provided new solutions for the intelligent control of hybrid electric powertrain systems with their powerful nonlinear modeling capabilities, data-driven optimization, and adaptive learning capabilities. This paper systematically reviews the research progress of artificial intelligence algorithms in hybrid electric powertrain systems. First, the architecture classification of hybrid electric powertrain systems is introduced. Secondly, the advantages and disadvantages of rule-based and optimization-based energy management strategies are summarized. Then, the existing research on the application of artificial intelligence algorithms in hybrid electric powertrain systems is systematically reviewed, and the advantages, disadvantages, and specific applications of various algorithms are analyzed in detail. Finally, the future application direction of artificial intelligence algorithms in hybrid electric powertrain systems is prospected.

关键词： hybrid electric powertrain system artificial intelligence algorithms deep learning reinforcement learning energy management

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artificial intelligence algorithms optimize immersion boiling heat transfer strategies to mitigate thermal runaway of lithium-ion batteries

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ETRANSPORTATION 2025年 24卷

作者： Lyu, Peizhao An, Zhenhua Li, Menghan Liu, Xinjian Feng, Xuning Rao, Zhonghao Hebei Univ Technol Hebei Engn Res Ctr Adv Energy Storage Technol & Eq Sch Energy & Environm Engn Tianjin 300401 Peoples R China Hebei Univ Technol Sch Energy & Environm Engn Hebei Key Lab Thermal Sci & Energy Clean Utilizat Tianjin 300401 Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China

Thermal runaway (TR) of lithium-ion batteries is the main cause of fire accidents in Electric Vehicles (EVs) and Energy Storage Stations (ESSs). Mitigating the TR is crucial for keeping safety of EVs and ESSs. The immersion boiling heat transfer technology is a promising candidate for mitigating TR of lithium-ion batteries. In this paper, to address the TR issue induced by tab-overheating at the positive tab of pouch-type lithium-ion batteries, a coupled model, considering electro-thermal model, lumped TR model and boiling heat transfer model, was applied to investigated the mechanism of mitigating TR for pouch-type lithium-ion batteries. Besides, the artificial intelligence (AI) algorithms were applied to analyze the importance of parameters, predict the optimum surface heat flux of batteries and then optimize the key parameters of coolants to reinforce immersion boiling heat transfer performance. The results exhibit that the immersion boiling technology can mitigate TR issue of pouch-type lithium-ion batteries induced by tab overheating. Besides, the importance analysis of parameters of coolants shows that the density, viscosity, and specific heat capacity are the top three parameters that affect the mitigating performance. The AI algorithms behaved a good performance in evaluating and optimizing the mitigating performance for TR of lithium-ion batteries. Hence, this work can provide a refence for improving the safety of EVs and ESSs.

关键词： Thermal runaway Immersion boiling heat transfer artificial intelligence algorithms Battery thermal management Lithium-ion batteries

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Analysis of facial recognition attendance technology based on artificial intelligence algorithms in political course e-learning teaching

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ENTERTAINMENT COMPUTING 2025年 52卷

作者： Chen, Lu Hanjiang Normal Univ Sch Hist Culture & Tourism Shiyan 442000 Peoples R China

The lag in course construction has led to some teaching content and methods not being well adapted to the characteristics of online teaching. Collected student face data and advanced facial recognition algorithms to automatically recognize student avatars, ensuring the accuracy of each student's identity. During the course, facial recognition attendance technology will automatically recognize students' attendance status and record it, thereby obtaining attendance data at any time during the teaching process. The attendance records of students are generated in real-time and easily imported into the academic affairs system for management and statistics. By applying facial recognition attendance technology, teachers can understand students' attendance in real-time and take timely measures to improve their learning enthusiasm. Students also use this technology to more conveniently sign in, reducing potential omissions and errors in the attendance process.

关键词： artificial intelligence algorithms Face recognition Network Teaching Attendance System

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Application of artificial intelligence algorithms in Ultrasound Diagnosis of Thyroid Diseases

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Procedia Computer Science 2025年 261卷 1129-1135页

作者： Xiaoyan Deng Chengwu Liu School of Clinical Medicine Guangzhou Health Science College Guangzhou 510450 China School of Public Health and Health Professions Guangzhou Health Science College Guangzhou 510450 China

Objective: To explore the application effect of artificial intelligence algorithms in ultrasound diagnosis of thyroid diseases. Method: Select a certain number of suspected thyroid disease patients and use conventional ultrasound diagnostic methods and ultrasound diagnostic methods combined with artificial intelligence algorithms for detection and diagnosis. Compare the accuracy, specificity, sensitivity, and diagnostic time of two diagnostic methods. The results showed that artificial intelligence algorithm assisted diagnosis was superior to conventional ultrasound diagnosis in terms of accuracy, specificity, and sensitivity, while significantly shortening the diagnosis time. Conclusion: artificial intelligence algorithms can effectively improve the accuracy and efficiency of ultrasound diagnosis of thyroid diseases, which is of great significance for improving the diagnostic effectiveness of thyroid diseases.

关键词： artificial intelligence algorithms Ultrasonic diagnosis Thyroid disease

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Transformation Path of artificial intelligence algorithms and Intelligent Engineering Management

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Procedia Computer Science 2025年 261卷 520-528页

作者： Huaizhu Wang Shandong Polytechnic College Jining 272067 Shandong China

In response to the low accuracy problem faced by the current engineering management field in project schedule management and other aspects, this article introduces the research on artificial intelligence algorithms and intelligent transformation paths, aiming to optimize project management processes and improve efficiency. Firstly, the core issues of project progress, resource allocation, and risk control in engineering management can be systematically identified. Then, this article designs an intelligent decision support system based on machine learning and deep learning, constructs a multidimensional prediction model, and achieves project key milestone prediction, resource scheduling optimization, and potential risk analysis. Finally, an intelligent management framework that adapts to projects of different scales is introduced. Through data collection, real-time analysis, and reinforcement learning dynamic decision support mechanisms, iterative optimization of resource allocation and risk control is achieved. At time points 3, 4, 6, and 9, the probability of actual risk events occurring increases, and the prediction accuracy of the Bayesian network model significantly improves. Its predictions at these time points almost completely match the actual situation (the predicted values are the same as the actual values). The combination of artificial intelligence algorithms and intelligent transformation paths provides new development opportunities for the field of engineering management. By gradually building an intelligent management system, not only can the management level of engineering projects be effectively improved, but also the optimal allocation of resources, precise control of risks, and efficient achievement of project goals can be achieved.

关键词： artificial intelligence algorithms engineering management intelligent transformation path resource scheduling optimization

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Prediction of the shear capacity of reinforced concrete slender beams without stirrups by applying artificial intelligence algorithms in a big database of beams generated by 3D nonlinear finite element analysis

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COMPUTERS AND CONCRETE 2021年第6期28卷 433-447页

作者： Markou, George Bakas, Nikolaos P. Univ Pretoria Civil Engn Dept Struct Div Hatfield Campus ZA-0028 Pretoria South Africa RDC Informat Res & Dev Dept 2 Irous St Athens 10442 Attica Greece

Calculating the shear capacity of slender reinforced concrete beams without shear reinforcement was the subject of numerous studies, where the eternal problem of developing a single relationship that will be able to predict the expected shear capacity is still present. Using experimental results to extrapolate formulae was so far the main approach for solving this problem, whereas in the last two decades different research studies attempted to use artificial intelligence algorithms and available data sets of experimentally tested beams to develop new models that would demonstrate improved prediction capabilities. Given the limited number of available experimental databases, these studies were numerically restrained, unable to holistically address this problem. In this manuscript, a new approach is proposed where a numerically generated database is used to train machine-learning algorithms and develop an improved model for predicting the shear capacity of slender concrete beams reinforced only with longitudinal rebars. Finally, the proposed predictive model was validated through the use of an available ACI database that was developed by using experimental results on physical reinforced concrete beam specimens without shear and compressive reinforcement. For the first time, a numerically generated database was used to train a model for computing the shear capacity of slender concrete beams without stirrups and was found to have improved predictive abilities compared to the corresponding ACI equations. According to the analysis performed in this research work, it is deemed necessary to further enrich the current numerically generated database with additional data to further improve the dataset used for training and extrapolation. Finally, future research work foresees the study of beams with stirrups and deep beams for the development of improved predictive models.

关键词： artificial intelligence algorithms beams without stirrups design formulae machine learning reinforced concrete shear strength prediction

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A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms

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APPLIED ENERGY 2018年 220卷 480-495页

作者： Wang, Minggang Zhao, Longfeng Du, Ruijin Wang, Chao Chen, Lin Tian, Lixin Stanley, Eugene Nanjing Normal Univ Sch Math Sci Nanjing 210042 Jiangsu Peoples R China Nanjing Normal Univ Taizhou Coll Dept Math Taizhou 225300 Jiangsu Peoples R China Boston Univ Ctr Polymer Studies Boston MA 02215 USA Boston Univ Dept Phys Boston MA 02215 USA Jiangsu Univ Energy Dev & Environm Protect Strategy Res Ctr Zhenjiang 212013 Jiangsu Peoples R China Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China Northwestern Polytech Univ Sch Management Xian 710072 Shanxi Peoples R China

Forecasting the price of crude oil is a challenging task. To improve this forecasting, this paper proposes a novel hybrid method that uses an integrated data fluctuation network (DFN) and several artificial intelligence (AI) algorithms, named DIN-AI model. In the proposed DFN-AI model, a complex network time series analysis technique is performed as a preprocessor for the original data to extract the fluctuation features and reconstruct the original data, and then an artificial intelligence tool, e.g., BPNN, RBFNN or ELM, is employed to model the reconstructed data and predict the future data. To verify these results we examine the daily, weekly, and monthly price data from the crude oil trading hub in Cushing, Oklahoma. Empirical results demonstrate that the proposed DIN-AI models (i.e., DFN-BP, DEN-RBF, and DFN-ELM) perform significantly better than their corresponding single AI models in both the direction and level of prediction. This confirms the effectiveness of our proposed modeling of the nonlinear patterns hidden in crude oil prices. In addition, our proposed DEN-AI methods are robust and reliable and are unaffected by random sample selection, sample frequency, or breaks in sample structure.

关键词： Complex network artificial intelligence algorithms Crude oil price prediction

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Prediction of pore-scale clogging using artificial intelligence algorithms

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2023年第12期37卷 4911-4919页

作者： Lei, Chao Samari-Kermani, Mandana Aslannejad, Hamed Zech, Alraune UFZ Helmholtz Ctr Environm Res Permoserstr 15 D-04318 Leipzig Germany Univ Utrecht Dept Earth Sci Princetonlaan 8a NL-3584CB Utrecht Netherlands Univ Utrecht Copernicus Inst Sustainable Dev Princetonlaan 8a NL-3584CB Utrecht Netherlands

We use five established, but conceptually different artificial intelligence algorithms for analysing clogging and quantifying colloid transport at pore scale: artificial neural networks, decision tree, random forest, linear regression, and support vector regression. We test how these algorithm can predict clogging by interpolating physics based simulation data. Our training and test data set is based on results from Lattice Boltzmann simulations reproducing the physics of colloid transport through a typical pore throat present in glass beads or medium sized sand. We perform hyperparameter optimization through cross validation for all algorithms. The tree based methods have the highest Nash-Sutcliffe efficiencies among all tested algorithms with values mostly above 0.9 for the independent test data. The event of clogging can be predicted even with 100% accuracy. Our results indicate a non-linear, rather categorial nature of the (simulation) data. This is in contrast to the typical use of neural network algorithms for simulation data while tree based methods are often applied to observational data. We partly link this to the small size of our dataset. Our application of artificial intelligence in porous media research shows that time-consuming Lattice Boltzmann simulations can be easily supplemented and extended at small computational costs while predictability of clogging and quantitative effects of process specific parameters on colloidal transport are given with high reliability.

关键词： Clogging Porous media artificial intelligence algorithms Pore scale Colloid transport

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Carbon Footprint Management in Global Supply Chains: A Data-Driven Approach Utilizing artificial intelligence algorithms

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IEEE ACCESS 2024年 12卷 89957-89967页

作者： Huang, Rong Mao, Shuai Hubei Univ Automot Technol Sch Econ & Management Shiyan 442000 Hubei Peoples R China

This abstract introduces a data-driven approach to managing carbon footprints in global supply chains through the integration of artificial intelligence (AI) algorithms. With the pressing need for sustainable practices, understanding and mitigating carbon emissions throughout the supply chain has become imperative. This study proposes a comprehensive framework that harnesses the power of AI to analyze, optimize, and monitor carbon footprints at various stages of the supply chain. The proposed approach utilizes AI algorithms to gather, process, and analyze vast amounts of data related to carbon emissions, including transportation, manufacturing, and sourcing activities. By leveraging machine learning and optimization techniques, the framework identifies key areas for emission reduction and develops strategies to minimize environmental impact while maintaining operational efficiency. Through real-time monitoring and predictive analytics, this approach enables proactive decision-making, allowing companies to adapt quickly to changing environmental regulations and market dynamics. The integration of AI not only enhances the accuracy and reliability of carbon footprint assessments but also provides insights for continuous improvement and sustainability performance tracking. This research contributes to the advancement of sustainable supply chain management by offering a data-driven approach that empowers organizations to effectively manage their carbon footprints and contribute to a more environmentally conscious global economy.

关键词： Carbon footprint management global supply chains data-driven approach artificial intelligence algorithms sustainability performance tracking

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