Synonyme wurden verwendet für: learning
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121–140 von 167 Ergebnissen
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    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

    Interpretability Analysis of Shear Capacity in Reinforced Recycled Aggregate Concrete Beams Using Tree Models

    Li, Li / Qin, Yapeng / Zhang, Yang et al. | Springer Verlag | 2024
    Schlagwörter: Machine learning

    High Speed Rail Learning System (HSRLS) – Taking Advantage of Online Technologies in Railway Education

    Freier Zugriff
    Pasi T. Lautala, Ph.D., P.E. | DOAJ | 2015
    Schlagwörter: Web-based teaching and learning

    Hybrid Prediction Model of Engineering Classification of Slope Rock Mass Based on DCWA-EO-AdaBoost Model and BQ Method

    Wang, Han / Gao, Yongtao / Xie, Yongsheng et al. | Springer Verlag | 2024
    Schlagwörter: Machine learning

    A Hybrid Feature Selection-multidimensional LSTM Framework for Deformation Prediction of Super High Arch Dams

    Cao, Enhua / Bao, Tengfei / Li, Hui et al. | Springer Verlag | 2022
    Schlagwörter: Deep learning

    Evaluation of Machine Learning Algorithms for Classification of Infrastructure Elements in Complex Structures

    Yilmaz, Yalcin / Bayrak, Onur Can / Soycan, Arzu | Springer Verlag | 2024
    Schlagwörter: Machine learning

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

    Yu, Yong / Hu, Tianyu | Springer Verlag | 2024
    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

    Structural Deformation Sensing Based on Distributed Optical Fiber Monitoring Technology and Neural Network

    Hou, Gong-Yu / Li, Zi-Xiang / Wang, Kai-Di et al. | Springer Verlag | 2021
    Schlagwörter: Machine learning

    Prediction of Axial Capacity of RC Columns Reinforced with Ferro-cement Jacketing: A Data-driven Machine Learning Strategy

    Nishant / Arora, Harish Chandra / Kumar, Aman et al. | Springer Verlag | 2024
    Schlagwörter: Machine learning

    Presenting a Hybrid Scheme of Machine Learning Combined with Metaheuristic Optimizers for Predicting Final Cost and Time of Project

    Bakhshi, Reza / Moradinia, Sina Fard / Jani, Rasool et al. | Springer Verlag | 2022
    Schlagwörter: Hybrid machine learning

    Shrinkage Crack Detection in Expansive Soil using Deep Convolutional Neural Network and Transfer Learning

    Andrushia, A. Diana / Neebha, T. Mary / Umadevi, S. et al. | Springer Verlag | 2022
    Schlagwörter: Deep learning

    Railway Maintenance Management Using a Stochastic Geometrical Degradation Model

    Golroo, Amir | Online Contents | 2016
    Schlagwörter: Learning models (Stochastic processes)

    Effectiveness of an Immersive VR System for Construction Site Planning Education

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

    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

    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