Synonyme wurden verwendet für: learning
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1–20 von 75 Ergebnissen
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    SHapley Additive exPlanations for Explaining Artificial Neural Network Based Mode Choice Models

    Koushik, Anil / Manoj, M. / Nezamuddin, N. | Springer Verlag | 2024
    Schlagwörter: Deep learning , Machine learning

    Enhancing Vibration-based Damage Assessment with 1D-CNN: Parametric Studies and Field Applications

    Park, Soyeon / Kim, Sunjoong | Springer Verlag | 2024
    Schlagwörter: Deep Learning

    Machine Learning Based Compressive Strength Prediction Model for CFRP-confined Columns

    Yu, Yong / Hu, Tianyu | Springer Verlag | 2024
    Schlagwörter: Machine learning

    Crack Detection of the Urban Underground Utility Tunnel Based on Residual Feature Pyramid Attention Network

    Zhou, Yuan / Li, Chengwei / Wang, Shoubin et al. | Springer Verlag | 2024
    Schlagwörter: Residual learning

    Effectiveness of an Immersive VR System for Construction Site Planning Education

    Wang, Kun-Chi / Hsu, Liang-Yu | Springer Verlag | 2024
    Schlagwörter: Learning effect evaluation

    Sequential Prediction of the TBM Tunnelling Attitude Based on Long-Short Term Memory with Mechanical Movement Principle

    Wang, Ruirui / Xiao, Yuhang / Guo, Qian et al. | Springer Verlag | 2024
    Schlagwörter: Deep learning

    Ensemble-based Deep Learning Approach for Performance Improvement of BIM Element Classification

    Yu, Young Su / Kim, Si Hyun / Lee, Won Bok et al. | Springer Verlag | 2023
    Schlagwörter: Deep learning , Ensemble learning

    Seismic Acceleration Estimation Method at Arbitrary Position Using Observations and Machine Learning

    Lee, Kyeong Seok / Ahn, Jin-Hee / Park, Hae-Yong et al. | Springer Verlag | 2023
    Schlagwörter: Machine-learning regression , Supervised learning

    Pore Structure Identification Method for Pervious Concrete Based on Improved UNet and Fusion Algorithm

    Yu, Fan / Li, Kailang / Zhang, Hua et al. | Springer Verlag | 2023
    Schlagwörter: Deep learning

    A Framework for Improving Object Recognition of Structural Components in Construction Site Photos Using Deep Learning Approaches

    Park, Sang Mi / Lee, Jae Hee / Kang, Leen Seok | Springer Verlag | 2023
    Schlagwörter: Deep learning

    Development of Data-based Hierarchical Learning Model for Predicting Condition Rating of Bridge Members over Time

    Choi, Youngjin / Kong, Jungsik | Springer Verlag | 2023
    Schlagwörter: Deep learning

    Explainable Boosting Machine for Predicting Wind Shear-Induced Aircraft Go-around based on Pilot Reports

    Khattak, Afaq / Chan, Pak-wai / Chen, Feng et al. | Springer Verlag | 2023
    Schlagwörter: Machine learning

    Investigation of Meta-heuristics Algorithms in ANN Streamflow Forecasting

    Wei, Yaxing / Hashim, Huzaifa / Chong, K. L. et al. | Springer Verlag | 2023
    Schlagwörter: Machine learning

    What is the Impact of COVID-19 on Residential Water Use?

    Sung, Jang Hyun / Chung, Eun-Sung | Springer Verlag | 2023
    Schlagwörter: Deep learning

    Internal Defect Detection of Structures Based on Infrared Thermography and Deep Learning

    Deng, Lu / Zuo, Hui / Wang, Wei et al. | Springer Verlag | 2023
    Schlagwörter: Deep learning

    Do Perceptions of Hydrogen Energy Effect on Vehicle Preference? A Learning-Based Model Approach

    Kim, Woojin / Kim, Junghwa / Jang, Jeong Ah et al. | Springer Verlag | 2023
    Schlagwörter: Machine learning

    Semi-Supervised Land Cover Classification of Remote Sensing Imagery Using CycleGAN and EfficientNet

    Kwak, Taehong / Kim, Yongil | Springer Verlag | 2023
    Schlagwörter: Semi-supervised learning

    A Machine Learning Tool for Pavement Design and Analysis

    Yang, Guangwei / Mahboub, Kamyar C. / Renfro, Ryan L. et al. | Springer Verlag | 2023
    Schlagwörter: Machine learning

    PSO-based Machine Learning Methods for Predicting Ground Surface Displacement Induced by Shallow Underground Excavation Method

    Kong, Fanchao / Tian, Tao / Lu, Dechun et al. | Springer Verlag | 2023
    Schlagwörter: Machine learning methods