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

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ENERGIES 2025年第8期18卷

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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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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Analysis of quartz content in muck based on artificial intelligence algorithms and laser-induced breakdown spectroscopy in TBM tunneling

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2024年第8期83卷 1-23页

作者： Wang, Yaxu Liu, Bin Wang, Jiwen Meng, Qingyang Liu, Zhengyu Shandong Univ Inst Geotech & Underground Engn 17923 Jingshi Rd Jinan 250061 Peoples R China Shandong Univ Sch Qilu Transportat Jinan 250061 Peoples R China Shandong Univ Sch Civil Engn Jinan 250061 Peoples R China

Rapid and in-situ sensing of the geological conditions of the rock mass is crucial to the tunnel boring machine (TBM) excavation process. The changes in rock strength and abrasiveness directly affect the working status of the TBM cutters and the tunneling efficiency. The content of quartz, as one of the important mineral components of rock, has a direct correlation with the wear state of the cutters. However, the current analysis of rock minerals still lacks a timely and convenient calculation method. In this study, a quartz content prediction method combining laser-induced breakdown spectroscopy (LIBS), rock chemical analysis and artificial intelligence algorithm is proposed. The spectral intensities of the main elements of the rock are obtained by LIBS detection of the muck on the belt machine. Then the quantitative characterization relationship between rock elements and mineral composition is analyzed to establish a transformation matrix. Finally, the mapping relationship between rock major element content and spectral intensity is established based on the random forest algorithm of Bayesian optimization (BO-RF). Subsequently, a prediction model for the mineral content of rocks, including quartz, is established. Relying on the Zhujiang Delta Water Resources Allocation Engineering, the prediction effects of this method and the other two algorithms (BO-Catboost and PLSR) are compared and verified. The results show that the prediction error of quartz content is less than 13% for all three methods, with the smallest error of 10.79% for the proposed method in this study. This also confirm the reliability and engineering practicability of the method.

关键词： Tunnel boring machine Laser-induced breakdown spectroscopy Quartz content artificial intelligence algorithms

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Intelligent energy management and operation efficiency of electric vehicles based on artificial intelligence algorithms and thermal energy optimization

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2024年 55卷

作者： Wang, Ying Shandong Inst Commerce & Technol Coll Digital Mkt Ind Jinan 250103 Shandong Peoples R China

As the global concern for environmental protection and sustainable development intensifies, electric vehicles (EVs), as a representative of clean transportation, are gradually receiving attention. This study aims to explore an intelligent energy management system for electric vehicles based on artificial intelligence algorithms, and focuses on optimizing heat energy utilization to improve the overall operating efficiency of electric vehicles. This paper uses machine learning and deep learning technologies to build intelligent decision-making models, and realizes dynamic optimization of energy demand and heat recovery by analyzing real-time data of electric vehicles under different working conditions. At the same time, a set of thermal management system is designed, which combines the driving state and environmental conditions of the vehicle to optimize the thermal management strategy of the battery and the motor. The experimental results show that the intelligent energy management system based on this research shows good adaptability and stability under different climate conditions, and significantly reduces energy consumption. Therefore, the intelligent energy management system of electric vehicles based on artificial intelligence algorithm and thermal energy optimization effectively improves the operating efficiency of electric vehicles.

关键词： artificial intelligence algorithms Electric vehicles Energy management Operational efficiency

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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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ETHICAL EVALUATION AND OPTIMIZATION OF artificial intelligence algorithms BASED ON SELF SUPERVISED LEARNING

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第5期25卷 3594-3602页

作者： Deng, Ruoyu Zhao, Yang Chengdu Technol Univ Sch Marxism Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China

Active learning solves the problem of requiring a large amount of manpower and resources due to the large size of training samples. The core problem is how to select valuable samples to reduce annotation costs. Using neural networks as classifiers, most methods choose samples with large amounts of information without considering the issue of information redundancy between the selected samples. Through the study of redundancy issues, the author proposes a sample selection optimization method to reduce information redundancy. Using uncertainty methods to select samples with high information content to form a candidate sample set, and using latent variable vectors calculated in the network to represent sample information, the cosine distance between candidate samples is calculated using this vector to select subsets with large interval distance and low information redundancy. Compared with several uncertainty methods in the Mnist, Fashion mnist, and Cifar-10 datasets, this method reduces labeled samples by a minimum of 11% with the same sample accuracy. The higher the dimensionality of the feature vector calculated by CNN, the more candidate samples it contains, and the more information it contains. After being improved by self supervised learning algorithms, the effect becomes more significant. The more candidate samples are selected, the stronger the information redundancy. The better the performance of self supervised learning algorithms.

关键词： artificial intelligence algorithms Information redundancy Cosine distance Uncertainty method

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Application of magnetic resonance imaging and artificial intelligence algorithms in cancer screening

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SLAS TECHNOLOGY 2024年第6期29卷 100218页

作者： Guo, Jian Xue, Yu Shanghai Pudong Hosp Dept Gen Surg Shanghai 201300 Peoples R China

In this society with a high incidence of cancer, cancer screening has become an important method to reduce the incidence and mortality of cancer. Traditional cancer screening methods such as CT have certain limitations and are difficult to adapt to large-scale and periodic cancer screening scenarios. Magnetic resonance imaging technology is an effective auxiliary method in CT methods, which can achieve high image resolution at lower doses and lower costs. Therefore, magnetic resonance imaging has become the most popular imaging method in clinical practice and a key research direction in the field of medical imaging. Therefore, this article intends to conduct indepth research on the application of image feature extraction based on magnetic resonance imaging and artificial intelligence algorithms in cancer screening. This article introduces particle swarm optimization algorithm into the learning of artificial intelligence models and further improves it. And compared multiple algorithms, such as Chaos Particle Swarm Optimization, Genetic Particle Swarm Optimization, and Grey Wolf Algorithm, in order to verify the effectiveness and feasibility of the algorithm proposed in this paper. On this basis, the intelligent optimization algorithm was further improved and validated. Experimental results have shown that the new method proposed in this article has strong fault tolerance, and various functional modules of the cancer screening management system have been optimized and designed from five aspects: front-end, back-end, external, database, and infrastructure.

关键词： Magnetic resonance imaging Imaging images artificial intelligence algorithms Cancer screening

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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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