Synonyme wurden verwendet für: Deep Learning
Suche ohne Synonyme: keywords:("Deep Learning")

1–20 von 23 Ergebnissen
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    Deep BBN Learning for Health Assessment toward Decision-Making on Structures under Uncertainties

    Pan, Hong / Gui, Guoqing / Lin, Zhibin et al. | Springer Verlag | 2018
    Schlagwörter: deep learning

    Multi-band Feature Images Concrete Crack Segmentation Framework Using Deep Learning

    Zhou, Shuang Xi / Pan, Yuan / Guan, Jingyuan et al. | Springer Verlag | 2024
    Schlagwörter: Deep 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

    The Optimal ANN Model for Predicting Bearing Capacity of Shallow Foundations trained on Scarce Data

    Bagińska, Marta / Srokosz, Piotr E. | Springer Verlag | 2018
    Schlagwörter: deep learning

    Development of Point Cloud Data-Denoising Technology for Earthwork Sites Using Encoder-Decoder Network

    Choi, Yeongjun / Park, Suyeul / Kim, Seok | Springer Verlag | 2022
    Schlagwörter: Deep learning

    SEMA: A Site Equipment Management Assistant for Construction Management

    Tsai, Meng-Han / Yang, Cheng-Hsuan / Wang, Chen-Hsuan et al. | Springer Verlag | 2022
    Schlagwörter: Deep learning

    Deep Learning-Based Real-Time Crack Segmentation for Pavement Images

    Wang, Wenjun / Su, Chao | Springer Verlag | 2021
    Schlagwörter: Deep learning

    A Damage Localization Approach for Rahmen Bridge Based on Convolutional Neural Network

    Lee, Kanghyeok / Byun, Namju / Shin, Do Hyoung | Springer Verlag | 2020
    Schlagwörter: Deep learning

    Particulate Matter Estimation from Public Weather Data and Closed-Circuit Television Images

    Won, Taeyeon / Eo, Yang Dam / Sung, Hongki et al. | Springer Verlag | 2022
    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

    Deep Learning-Based Prediction of Fire Occurrence with Hydroclimatic Condition and Drought Phase over South Korea

    Sung, Jang Hyun / Ryu, Young / Seong, Kee-Won | Springer Verlag | 2022
    Schlagwörter: Deep learning

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

    Park, Soyeon / Kim, Sunjoong | Springer Verlag | 2024
    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

    Reliability of Jack-up against Punch-through using Failure State Intelligent Recognition Technique

    Lyu, Tao / Xu, Changhang / Chen, Guoming et al. | Springer Verlag | 2019
    Schlagwörter: deep learning

    Inverse Design and Application Periodic Barriers for Isolating Ambient Vibration Based on Deep Learning

    Feng, Li / Guo, Jinhong | Springer Verlag | 2024
    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

    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

    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

    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