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5th International Conference on Transportation Geotechnics

作者： Wang, Ze Zhou Al-Tabbaa, Abir Hakim, Bachar Indraratna, Buddhima Univ Cambridge Cambridge CB3 0FA England AECOM Ltd Dallas England Univ Technol Sydney Ultimo NSW 2007 Australia

ISBN: (纸本)9789819782154;9789819782130;9789819782123

The road network in many countries holds significant value, and the amount will continue to grow due to the rapid growth of cities and populations. Given this, it is crucial to improve knowledge of the existing road assets, based on which effective management strategies can be customized to maximize the useful service life of current road assets. A key aspect in achieving this goal is the employment of performance modelling, which forecasts future pavement performance. In recent years, the increasing popularity of data-driven approaches has propelled the development of advanced pavement performance models. In this paper, existing data-driven performance models developed globally for different types of pavements and various climate and environmental conditions are first summarized. The review on data-driven performance models then focuses on the capabilities of these models: (i) in handling the time-dependent nature of the data involved, and (ii) in utilizing the existing information available to engineers to forecast future pavement conditions. The objective of this review is to highlight the current state-of-the-art and challenges in data-driven performance modelling and conclude with potential directions and insights for driving innovation and research in the roads sector for practical applications.

关键词： Pavement management Performance model data-driven method

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An Online data-driven LPV Modeling method for Turbo-Shaft Engines

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ENERGIES 2022年第4期15卷 1255-1255页

作者： Gu, Ziyu Pang, Shuwei Zhou, Wenxiang Li, Yuchen Li, Qiuhong Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Jiangsu Prov Key Lab Aerosp Power Syst Nanjing 210016 Peoples R China

The linear parameter-varying (LPV) model is widely used in aero engine control system design. The conventional local modeling method is inaccurate and inefficient in the full flying envelope. Hence, a novel online data-driven LPV modeling method based on the online sequential extreme learning machine (OS-ELM) with an additional multiplying layer (MLOS-ELM) was proposed. An extra multiplying layer was inserted between the hidden layer and the output layer, where the hidden layer outputs were multiplied by the input variables and state variables of the LPV model. Additionally, the input layer was set to the LPV model's scheduling parameter. With the multiplying layer added, the state space equation matrices of the LPV model could be easily calculated using online gathered data. Simulation results showed that the outputs of the MLOS-ELM matched that of the component level model of a turbo-shaft engine precisely. The maximum approximation error was less than 0.18%. The predictive outputs of the proposed online data-driven LPV model after five samples also matched that of the component level model well, and the maximum predictive error within a large flight envelope was less than 1.1% with measurement noise considered. Thus, the efficiency and accuracy of the proposed method were validated.

关键词： turbo-shaft engine linear parameter-varying model data-driven method online sequential extreme learning machine (OS-ELM)

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data-driven methods for Predicting the State of Health, State of Charge, and Remaining Useful Life of Li-Ion Batteries: A Comprehensive Review

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2023年第7期24卷 1281-1304页

作者： Kim, Eunsong Kim, Minseon Kim, Juo Kim, Joonchul Park, Jung-Hwan Kim, Kyoung-Tak Park, Joung-Hu Kim, Taesic Min, Kyoungmin Soongsil Univ Sch Mech Engn 369 Sangdo Ro Seoul 06978 South Korea Soongsil Univ Dept Elect Engn 369 Sangdo Ro Seoul 06978 South Korea Texas A&M Univ Kingsville Dept Elect Engn & Comp Sci Kingsville TX USA

Lithium-ion batteries are widely used in electric vehicles, electronic devices, and energy storage systems owing to their high energy density, long life, and outstanding performance. However, various internal and external factors affect the battery performance, leading to deterioration and ageing. Accurately estimating the state of health (SOH), state of charge (SOC), and remaining useful life (RUL) of batteries is challenging owing to complex operating characteristics and changing internal physical parameters. With the increasing availability of shared battery data and improved computer performance, the use of data-driven methods for battery health estimations and RUL predictions has gained popularity. We provide a comprehensive review of several studies in which data-driven methods were used for SOC and SOH estimation and RUL prediction. Specifically, we focus on the importance of open battery-cycling databases, various prediction methods used, and results obtained using each of these methods. Moreover, we aim to facilitate further research by providing a comprehensive description of the current state-of-the-art methods employed in battery health estimation and RUL prediction using open databases and machine-learning algorithms. Thus, we hope that this review will help researchers to develop accurate and reliable predictive models for battery health assessment in the future.

关键词： Lithium-ion batteries State of charge State of health Remaining useful life data-driven method

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data-driven methods for estimating the effective thermal conductivity of nanofluids: A comprehensive review

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2019年 131卷 1211-1231页

作者： Zendehboudi, Alireza Saidur, R. Mahbubul, I. M. Hosseini, S. H. Tsinghua Univ Dept Bldg Sci Beijing 100084 Peoples R China Sunway Univ Sch Sci & Technol Res Ctr Nanomat & Energy Technol RCNMET 5 Jalan Univ Petaling Jaya 47500 Selangor Darul Malaysia KFUPM Res Inst Ctr Res Excellence Renewable Energy CoRERE Dhahran 31261 Saudi Arabia Ilam Univ Dept Chem Engn Ilam 69315516 Iran

There is a growing body of work in the field of nanofluids and several investigations have been conducted on their thermal conductivities. While the experimental works require considerable investments to provide a highly equipped laboratory and proper instruments, the predictive models are useful to promote understanding nanofluids under different operational conditions. However, the restrictions on the traditional predictive models require scholars to develop reliable models that are capable of simulating the thermophysical properties of nanofluids. data-driven models have attracted huge attention and been widely applied in various fields. There is no specific review on application of data-driven models in thermal conductivity of nanofluids till now and thus this is the first review paper which gives a state of the art review of research progress in this field of study. It was identified that the effective thermal conductivity of nanofluids is reflected well by the new predictive models. The authors believe that the data-driven models are fast, reliable, simple-to-use, and practical. This may open a new door for the scientists, engineers, and researchers in the field of nanofluid for calculating the effective thermal conductivity with excellent precision. This investigation leads to the recognition of the main problems and the opportunities in the research area for the future works. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Nanofluids Thermal conductivity data-driven method Computational modeling Artificial neural networks

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Exploring the effective implementation of population-based SHM in existing buildings. Part I: Structural condition assessment

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ENGINEERING STRUCTURES 2025年 322卷

作者： Astorga, A. Gueguen, P. Beth, M. Bessoule, N. Univ Gustave Eiffel Univ Grenoble Alpes Univ Savoie Mont Blanc ISTerreCNRSIRD Grenoble France Sixense Paris France

Despite major critical progress over the past few decades, condition-based decision-making concerning the immediate occupancy or efficient operation of an existing building continues to face unresolved issues. The key issue is to determine the structural condition and its residual capacity using data-driven-based methods without knowledge of its life cycle since design (ageing, extreme events, etc.). Environmental and operational variations, boundary conditions and a lack of damage-state building data lead to further uncertainties in the actual assessment of the structural capacity and affect the effectiveness of implementing structural health monitoring (SHM) solution. Recently, population-based SHM has been developed to address some of these issues to enable transfer from data collected from a group of nominally identical buildings, and to model missing data for the target building. This study presents the Build'HealthTM framework for operational data-driven SHM, considering populations of buildings evaluated under operational conditions. Fifty-five period-height empirical models obtained from methods based on ambient vibrations recorded in nominally identical reinforced concrete buildings were first identified in the literature to define the building population models, along with design features (such as boundary conditions, geometry or material design). The shift in structural condition of the target building is inferred from the nominally identical building population, after adding empirical variability in the resonance period due to temperature, assumed herein to be the main environmental cause of structural variation. This work presents the framework implemented and its application to five target buildings under different monitoring and structural conditions. This manuscript is the first of a two-part article on population-based SHM relative to structural condition assessment (part I) and damage-feature classification (part II) presenting the Build'HealthTM c

关键词： Population-based structural health monitoring Transfer of knowledge data-driven method Building damage detection Existing buildings

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A comprehensive review on uncertainty modeling methods in modern power systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2025年 166卷

作者： Wang, Zhaoyuan Bu, Siqi Wen, Jiaxin Huang, Can Hong Kong Polytech Univ Dept Elect & Elect Engn Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen Guangdong Peoples R China Hong Kong Polytech Univ Res Ctr Grid Modernisat Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Int Ctr Urban Energy Nexus Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy Kowloon Hong Kong Peoples R China China Southern Power Grid Energy Dev Res Inst Guangzhou Guangdong Peoples R China Pacific Gas & Elect Co Oakland CA USA

In recent years, modern power systems integrated with renewable energy sources and communication technologies have been rapidly developed. However, the randomness and intermittency of renewable energy sources, together with the unexpected disturbances in the vulnerable cyber layer, introduce uncertainties into power systems, which pose huge threats to the stable operation of modern power systems and should be carefully considered. Moreover, there is a lack of a comprehensive review of uncertainty modeling in modern power systems, which is one of the significant tasks in quantifying uncertainties. In this review, the methods and respective characteristics of uncertainty modeling in modern power systems are analyzed. According to the outputs of methods, they are categorized as model-driven and data-driven methods. The existing uncertainties in modern power systems and the respective modeling methods are investigated from the aspects of the physical layer, cyber layer, and economic and social layer. The applications of uncertainty modeling methods in modern power system operation are summarized from the aspects of steady state, dynamic, and risk analysis of modern power systems. Also, the prospective future research topics are recommended.

关键词： Uncertainty modeling Model-driven method data-driven method Renewable energy Modern power system

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Estimating dynamic compressive strength of rock subjected to freeze-thaw weathering by data-driven models and non-destructive rock properties

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NONDESTRUCTIVE TESTING AND EVALUATION 2025年第1期40卷 116-139页

作者： Zhou, Shengtao Lei, Yu Zhang, Zong-Xian Luo, Xuedong Aladejare, Adeyemi Ozoji, Toochukwu China Univ Geosci Fac Engn Wuhan Peoples R China Univ Oulu Oulu Min Sch Oulu Finland

The dynamic compressive strength (DCS) of frozen-thawed rock influences the stability of rock mass in cold regions, especially when rock masses are possibly disturbed by dynamic loads. Laboratory freeze-thaw weathering treatment is usually time-consuming, and the dynamic strength test is destructive. Therefore, this paper attempts to quickly predict the DCS of frozen-thawed sandstones using data-driven methods, non-destructive rock properties, and basic environmental parameters. The sparrow search algorithm (SSA), gorilla troops optimiser, and dung beetle optimiser were chosen to develop two hyperparameters in the random forest (RF). The classic RF, back propagation neural network, and support vector regression models were taken as the control group. These six models were developed to predict the DCS. Their prediction results were compared. Finally, the sensitivity analysis was carried out to assess the significance of all input variables. The results indicate that the SSA - RF model yields the best prediction result, and three optimised models have better performance than single machine-learning models. Strain rate, dry density, and wave velocity are found to be the three most important parameters in DCS prediction, which further indicates that there is also a strong correlation between the characteristic impedance of the rock and the DCS.

关键词： Dynamic compressive strength freeze-thaw weathering data-driven method non-destructive rock property random forest

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Remaining useful life prediction of PEMFCs based on mode decomposition and hybrid method under real-world traffic conditions

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ENERGY 2025年 314卷

作者： Chen, Li Yang, Jibin Wu, Xiaohua Deng, Pengyi Xu, Xiaohui Peng, Yiqiang Xihua Univ Sch Automobile & Transportat Vehicle Measurement Control & Safety Key Lab Prov Engn Res Ctr New Energy Vehicle Intelligent C Chengdu 610039 Sichuan Peoples R China

Accurately predicting the remaining useful life (RUL) of proton-exchange membrane fuel cells (PEMFCs) is essential for the health management of vehicle-oriented PEMFCs. Utilizing operational data of a demonstration PEMFC city bus in Chengdu, China, an RUL prediction method for PEMFCs that combines mode decomposition and a hybrid prediction model is developed. The improved complete ensemble empirical mode decomposition with adaptive noise is used to decompose and reconstruct the data into high- and low-frequency components to cope with the voltage recovery phenomenon. The hybrid prediction model comprises two phases. In the datadriven phase, a bidirectional long short-term memory (BiLSTM) neural network is used to predict the lowfrequency components, while a mixed model comprising a convolutional neural network, BiLSTM, and an attention mechanism is used to predict the high-frequency components. The dung beetle optimization algorithm is used to optimize the parameters of the mixed model. The prediction results of the data-driven phase are then used as observed values in the model-driven phase, which involves applying the adaptive unscented Kalman filter to a voltage degradation model and obtaining the final prediction. The results show that the mean absolute percentage error of the proposed method is less than 1 % under real-world traffic conditions.

关键词： Proton-exchange membrane fuel cell Remaining useful life Mode decomposition Hybrid method data-driven method Model-driven method

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Automatic data-driven modeling of plate and beam elements based on the first principles of elasticity

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THIN-WALLED STRUCTURES 2025年 209卷

作者： Wu, Zhetong Qiu, Yisong Jia, Haikun Zhang, Hanbo Ye, Hongfei Zheng, Yonggang Dalian Univ Technol Optimizat & CAE Software Ind Equipment State Key Lab Struct Anal Sch Mech & Aerosp EngnDept Engn Mech Dalian 116024 Peoples R China China Elect Power Res Inst Natl Key Lab Renewable Energy Grid Integrat Beijing 100192 Peoples R China

The establishment of various basic mechanical elements is of great significance for qualitative analysis of the deformation mechanism of engineering structures with typical geometric features, as well as for rapid and accurate predictions of their mechanical behaviors. Although classical beam and plate models have been extensively developed and widely used in structural mechanics analysis, the expressions of the key parameter in shear deformation theories remain a topic of ongoing research, with no consensus yet reached. In this paper, the highprecision beam and plate models are established by the data-driven modeling method based on the first principles of elasticity. The governing equations of the data-driven models are automatically discovered from finite element simulation data without making any assumptions or approximations about the deformation of the structures. The established models are found to contain the high-order derivatives of the load, which are consistent with the shear deformation theories. The parameters of the governing equations are related to the position of the load, which has never been studied in previous work. Several representative numerical examples demonstrate that the established data-driven models are of higher accuracy compared to classical shear deformation models. This work provides an effective solution for establishing high-precision mechanical models of mechanical elements, and also offers a promising method for modeling and analyzing complex multiphysics systems and engineering structures.

关键词： Mechanical model Basic mechanical element data-driven method Shear deformation theory

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data-driven reliability analysis of district heating systems for asset management applications: A review

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SUSTAINABLE CITIES AND SOCIETY 2025年 118卷

作者： Rafati, Amir Tahavori, Maryamsadat Shaker, Hamid Reza Univ Southern Denmark Maersk Mc Kinney Moeller Inst Fac Engn SDU Ctr Energy Informat Odense Denmark Tech Univ Denmark Sect Energy Technol & Comp Sci DTU Engn Technol Ballerup Denmark

As district heating systems (DHSs) age, reliability analysis becomes increasingly vital for achieving energy efficiency, meeting sustainability goals, and ensuring the resilience of these urban infrastructures. Traditional reliability and asset management methods are inadequate, as they fail to incorporate the new measurements and varying asset conditions. To address this issue, advanced data-driven approaches that leverage real-time data and cutting-edge analytics are essential. This study thoroughly reviews data-driven techniques utilized for reliability analysis and assessment of DHSs. Additionally, it explores asset management strategies with an emphasis on data-driven reliability-based asset management. The literature review highlights the uniqueness of asset management approaches for each DHS, influenced by factors such as materials, age, environmental factors, and operational variables. The study discusses the existing challenges for reliability-based asset management in DHS and suggests potential research directions.

关键词： Asset management District heating Reliability assessment Failure probability data-driven method Machine learning

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