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machine learning-based seismic fragility curves of regular infilled RC frames

JOURNAL OF BUILDING ENGINEERING 2025年 99卷

作者： He, Dianjing Cheng, Xiaowei Liu, Hang Li, Yi Zhang, Haoyou Ding, Zhaowang Beijing Univ Technol Key Lab Urban Secur & Disaster Engn Minist Educ Beijing 100124 Peoples R China Beijing Bldg Construct Res Inst Co Ltd Beijing 100039 Peoples R China Beijing Construct Engn Qual First Testing Inst Co Beijing 100039 Peoples R China

Infilled reinforced concrete (RC) frame building is prone to collapse during earthquakes, highlighting the importance for seismic vulnerability assessment of infilled RC frame. Incremental Dynamic Analysis (IDA) is widely used for vulnerability assessment, which required to conduct nonlinear time history (NTH) analysis, leading to high computational costs and significant time consumption for extensive analyses. In this study, machine learning (ML) models which could accurately predict structural response of infilled RC frames are firstly developed, and are used to replace the NTH analysis and quickly obtain the structural dynamic response. Thereafter, ML methods are employed instead of the IDA process to directly predict the dynamic response of structures and subsequently establish fragility curves. This approach significantly reduces computational and time costs, better balances accuracy and efficiency. This paper firstly develops a seismic damage database for infilled RC frames utilizing the OpenSees finite element model. Two methods, IDA and ML, are employed to establish fragility curves for infilled RC frames. The results indicate that eight ML models can predict the structural response excitated by scaled ground motion, with the Artificial Neural Network (ANN) model demonstrating the highest prediction accuracy. Compared to other ML models, the fragility curves predicted by ANN model are generally more consistent with those derived from the IDA method. Eventually, ML method is applied to actual damaged buildings in Taiwan earthquake, indicating that the method of ML- based seismic fragility curves could accurately predict the damage state of infilled RC frames.

关键词： machine learning method Fragility curves Incremental dynamic analysis Infilled RC frames Finite element model

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Reconstructing the problem of galloping monitoring of traditional complex analytical mechanism into a prediction method for machine learning algorithm modeling 3

Reconstructing the problem of galloping monitoring of tradit...

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3rd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Cheng, YongFeng Han, Jingshan Zhang, Jing Hao, Wei China Elect Power Res Inst Beijing 102401 Peoples R China Beijing BeYes Technol Co Ltd Beijing 100083 Peoples R China

ISBN: (纸本)9781538645093

To avoid a series of shortcomings of the traditional complex analytical mechanism, this paper selects the algorithm suitable for the transmission line galloping prediction model according to the characteristics of the traverse movement and the characteristics of the related galloping history data. After mining effective information from the selected and preprocessed data, the one class SVM algorithm is used to carry out unsupervised learning of the galloping history data. The Bagging algorithm is used in the ensemble learning algorithm to learn the classifier, avoiding the over fitting, and improving the anti-noise ability of the machine learning algorithm and the noise ability of the machine learning algorithm. The generalization ability, using the K folding cross validation method to verify the data of the galloping monitoring platform of the China Electric Power Research Institute, and using F1-score to describe the performance of the traverse galloping early warning model, and verify the effectiveness of the machine learning method used in this paper in the aspect of galloping prediction.

关键词： Transmission Line Galloping machine learning method One Class SVM Ensemble learning F1-Score

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A Novel Mice Interaction Recognition method By Using machine learning 11

A Novel Mice Interaction Recognition Method By Using Machine...

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11th International Conference on Digital Image Processing (ICDIP)

作者： Pan, Xu Zhu, Yuesheng Luo, Guibo Guo, Biao Peking Univ Shenzhen Grad Sch Commun & Informat Secur Lab Shenzhen 518055 Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

Mice interaction recognition has been widely employed in animal observation experiments since it can provide useful biological signals for researchers to identify the social behaviors of animal models. Rather than manual observation, automatic observation systems based on computer vision can be used to detect and track the mice behaviors dynamically, which has become popular in mice interaction recognition. In order to enhance the accuracy of recognition, a novel mice interaction recognition method by using machine learning is proposed in this paper. A new elliptical model is developed to fit every mouse and to extract its motion and shape features. For selecting the optimal feature, we investigate the influence of different features on the result. We improve the average recognition result on the novel dataset RatSI.

关键词： Mice interaction recognition computer vision machine learning method hidden markov model

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Damage prediction of hull structure under near-field underwater explosion based on machine learning

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APPLIED OCEAN RESEARCH 2025年 154卷

作者： He, Zhenhong Chen, Xiaoqi Zhang, Xiaoqiang Jiang, Yongbo Ren, Xianben Li, Ying Beijing Inst Technol State Key Lab Explos Sci & Technol Beijing 100081 Peoples R China Beijing Inst Technol Inst Adv Struct Technol Beijing Key Lab Lightweight Multifunct Composite M Beijing 100081 Peoples R China Wuhan Univ Technol Sch Naval Architecture Ocean & Energy Power Engn Wuhan 430063 Peoples R China

Underwater explosions generate diverse loads, encompassing shock waves and bubble pulsation, and pose a substantial threat to naval vessels. In this study, finite element simulations were carried out to evaluate the response of the hull to different underwater explosion cases, including varying TNT masses and standoff distances. With the increase of the standoff distance, the deformation of the bottom plate was observed to exhibit a multi-peak phenomenon with a sharp decrease followed by a slight increase and subsequent reduction. Based on a large hull damage dataset obtained from the finite element simulations, a deep neural network (DNN) model was trained using machine learning (ML) algorithms. The deformation values predicted by the model were found to closely match the simulation results. This method provides an approach for the rapid prediction of the deformation of hull structures under near-field underwater explosions.

关键词： machine learning method Deep neural network Near-field underwater explosion Multi-peak phenomenon

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Statistical analysis of absolute photopeak efficiency of clover HPGe detectors

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NUCLEAR INSTRUMENTS & methodS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2025年 1072卷

作者： Dey, P. Palit, R. Das, Biswajit Kundu, A. Malik, Vishal Tata Inst Fundamental Res Dept Nucl & Atom Phys Mumbai 400005 India

The variation of absolute photopeak efficiency of an array of clover HPGe detectors as a function of y-ray energy has been obtained using statistical model analysis. machine learning technique has been implemented for determining the energy-dependent polynomial of photopeak efficiency. The distribution of parameters of the best-fit model has been deduced by incorporating Markov chain Monte Carlo method working with the basic framework of Bayesian analysis which also provides the correlation between the parameters. This method has the advantage of calculating the posterior probability distribution by maximizing the likelihood function of the experimental data over standard least-squares method. Excellent agreement among the results procured from machine learning, Markov chain Monte Carlo, and least-squares method has been achieved and the correct prediction of the nature of the variation extrapolation region as well as interpolation region has been discussed.

关键词： Absolute photopeak efficiency y-ray detectors machine learning method Bayesian analysis Markov chain Monte Carlo method

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Prediction of nonuniform large deformation in deep layered rock tunnels: Comprehensive application of data-driven and theoretical models

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2025年 158卷

作者： Song, Tianxiang Zhou, Yangyi Chen, Tao Sun, Bentong Northeastern Univ Sch Resources & Civil Engn Key Lab Minist Educ Safe Min Deep Met Mines Shenyang 110819 Peoples R China Northeastern Univ Key Lab Liaoning Prov Deep Engn & Intelligent Tech Shenyang 110819 Peoples R China Sinohydro Bur 7 Co Ltd Chengdu 610213 Sichuan Peoples R China

The irregular and significant deformation often encountered in the construction of deep-layered rock tunnels presents a notable challenge. Precisely assessing the anisotropic true triaxial compression (TTC) strength of layered rocks is pivotal for anticipating nonuniform deformation in these tunnels. machine learning (ML) methods offer distinct advantages in terms of cost-effectiveness and accuracy when predicting rock strength. This study utilized four ML algorithms to forecast the TTC strength of layered rocks. The input parameters encompassed minimum and intermediate principal stresses, loading angles (beta, omega), and maximum and minimum uniaxial compression strengths of the surrounding rock. The dataset comprised 1548 strength data points derived from laboratory tests on 42 types of layered rocks, along with 47,476 physics-guided data points generated using MATLAB based on the rock failure criteria proposed by Feng et al. (2020) and Liu et al. (2023a). The entire dataset was partitioned into training and validation sets, with an additional test set employed to train and assess the performance of the proposed models. Additionally, an analytical solution for the redistribution stress state of the surrounding rock in deep-layered rock tunnels was derived. Introducing a nonuniform deformation prediction index (NDPI) based on the predicted TTC strength and stress state of the surrounding rock further enhanced the analysis. Evaluating NDPI values at various locations of tunnel sections offers a swift and effective means of anticipating nonuniform deformation during the tunnel design phase in layered rock tunnels. To validate its efficacy and reliability, the proposed method was applied to a deep carbonaceous slate tunnel.

关键词： Layered rock tunnel machine learning method Anisotropic TTC strength prediction Asymmetric large deformation prediction

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Application of the Woods-Saxon potential in studying quadrupole and octupole excited states using machine learning

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Chinese Physics C 2025年第4期49卷 152-163页

作者： Hadi Sobhani Yan-An Luo School of Physics Nankai UniversityTianjin 300071China

this study,the energy bands of quadrupole and octupole excited states are *** is achieved by employing the Bohr Hamiltonian,incorporating quadrupole and octupole deformations whose variables are accurately ***,the Woods-Saxon potential is added to the *** this problem cannot yield suitable solutions using conventional approximations,we solve it numerically using machine learning.A detailed description is given of how wave functions and their associated energies are *** this procedure,we demonstrate how machine learning aids us in easily accomplishing our *** examine and analyze the energy spectrum and possible multipole transitions for candidate isotopes^(226)Ra and^(226)Th.

关键词： quadrupole and octupole excited states Bohr Hamiltonian Woods-Saxon potential machine learning method

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machine learning-based analysis of workers' exposure and detection to volatile organic compounds (VOC)

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2025年 1-17页

作者： Eid, A. Jodeh, S. Chakir, A. Hanbali, G. Roth, E. An Najah Natl Univ Dept Math Nablus Palestine An Najah Natl Univ Dept Chem POB 7 Nablus Palestine Univ Reims Grp Spectrometrie Mol & Atmospher GSMA UMR 7331 CNRS BP 1039Moulin Housse F-51687 Reims 02 France

This article examines the ability of supervised learning methods such as support vector machines, random forest (RF), and neural networks to characterize individuals exposed to volatile organic compounds (VOCs) using VOC measurements collected from a carpentry workshop in a Palestinian village. The analysis reveals significant differences in VOC measurements among the categories of participated individuals, emphasizing the importance of VOCs as indicators. The predictive models achieved high accuracy in identifying gender, smoking status, and age, with the RF model reaching 100% accuracy in age prediction and 98% accuracy in smoking status prediction. In terms of VOC importance, "Toluene.2.4. diisocyanate" and "Propanal" were the top VOCs for predicting age, while "Toluene.2.4..diisocyanate" and "Bromodichloromethane" were crucial for predicting smoking status. For gender prediction, "Toluene.2.4..diisocyanate" and "Ethylbenzene" emerged as key VOCs. The study contributes to the field of environmental sciences and air pollution research, as well as providing practical recommendations for decision-makers. These recommendations emphasize the need to organize carpentry workshops and similar facilities in a way that reduces pollution and safely protects the health of nearby communities. By applying machine learning techniques, this research provides insights valuable for informed decision-making and for advancing business and environmental management practices in related industries.

关键词： Volatile organic compounds(VOC) Air pollution detection machine learning method Support vector machine (SVM) Random forest (RF) Artificial neural network (ANN) Good health and well-being

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Prediction of bond strength between fibers and the matrix in UHPC utilizing machine learning and experimental data

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MATERIALS TODAY COMMUNICATIONS 2025年 42卷

作者： Huang, Jia-Xing Shi, Xian-Zeng Zhang, Ning Hu, Yu-Qing Wang, Jing-Quan Anhui Jianzhu Univ Sch Civil Engn Hefei 230601 Peoples R China Southeast Univ Sch Civil Engn Nanjing 210096 Peoples R China

The bond strength between fibers and the matrix plays a crucial role in the tensile strength and post-cracking performance of ultra-high-performance concrete (UHPC). However, existing studies have not attempted to utilize machine learning models for the prediction of bond strength. In this study, machine learning techniques were employed to predict the bond strength of fibers in UHPC. A dataset comprising 658 experimental records was compiled and advanced unsupervised Isolation Forest techniques were utilized to identify and remove outliers, thereby ensuring the accuracy and reliability of the data. Six machine learning models, including ANN, GBDT and XGBoost are utilized in this study, with a focus on evaluating their performance in predicting the maximum pull-out force of fibers. The results indicate that the XGBoost model exhibits exceptional predictive performance, achieving R2 value of 0.98, which demonstrates the model's capability to accurately predict the fiber pull-out process. Furthermore, feature importance analysis, visualized through advanced techniques, reveals the significant influence of fiber tensile strength on the pull-out force. A new equation is proposed to predict the maximum pull-out force of fibers and correction factors for different fiber shapes are introduced, significantly enhancing the precision of the calculations. The newly proposed predictive equation has R2 value of 0.72, which increases to 0.74, 0.77, and 0.86 after the introduction of shape correction factors, significantly enhancing the accuracy of the computed results. This study not only provides an efficient and reliable method for predicting fiber bond strength in UHPC but also offers an innovative tool for calculating fiber pull-out force.

关键词： UHPC Steel fiber Bond strength machine learning method Prediction equation

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Unveiling micro-macro links in asphalt: An in-depth analysis of characterization and simulation approaches

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FUEL 2025年 379卷

作者： Zhang, Hongfei Zheng, Fusen Zhang, Jiupeng Hu, Dongliang Yu, Xiang Chen, Zixuan Pei, Jianzhong Changan Univ Sch Highway Xian 710064 Peoples R China Changan Univ Key Lab Special Area Highway Engn Minist Educ Xian 710064 Peoples R China

Examining the intricate relationship between micro-components and macro-performance represents a pivotal focus within asphalt materials research. This review offers a comprehensive discussion on the micro-macro correlations in asphalt, delineating these correlations from two primary perspectives: characterization approaches and molecular dynamic (MD) simulation. This review begins by outlining foundational principles and assessment methodologies concerning saturates, aromatics, resins, and asphaltenes (SARA) components separation method, as well as techniques such as Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), and gel permeation chromatography (GPC) methods. The application of the above methods in examining the relationship between chemical structure, components, and performance is highlighted, offering both qualitative and quantitative insights. The review then considers how MD simulation contributes to the investigation of SARA fractions interactions and analyses of physical, rheological, self-healing, and interfacial adhesion performance in asphalt materials, within the broader context of the micro-macro links. Finally, a proposition is forwarded to amalgamate characterization methodologies with data obtained from MD simulations, aiming to establish a comprehensive material information database. This initiative seeks to develop a robust structure-activity relationship model for asphalt, utilizing advanced machine learning (ML) methodologies. This review not only advances comprehension regarding the intricate components of asphalt but also furnishes a foundation for the targeted design and optimization of superior-quality asphalt materials.

关键词： Asphalt binder Micro-macro links Characterization approaches Molecular dynamic simulation machine learning method

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