Sökning: "Bridge Damage Detection"

Visar resultat 1 - 5 av 7 uppsatser innehållade orden Bridge Damage Detection.

  1. 1. Development of a model-free damage detection algorithm using AR-models and its application to a riveted steel railway bridge

    Master-uppsats, KTH/Bro- och stålbyggnad

    Författare :Mattis Johannes Frenz; [2022]
    Nyckelord :;

    Sammanfattning : Bridges are one of the most important structures in our infrastructural system today.The modern society depends on these structures which in majority are nearingthe end of their design life. Therefore, the need for monitoring of bridges is increasing. LÄS MER

  3. 2. Implementering av Structural Health Monitoring : SHM - system för detektering och övervakning av vanligt förekommande skador på betongbroar

    Kandidat-uppsats, KTH/Byggteknik och design

    Författare :Jonathan Le Guillarme; Jakob Lindstam; [2019]
    Nyckelord :Structural Health Monitoring; Bridge Maintenance; Monitoring With Sensor Technology; Fibre Optical Sensors; Acoustic Emission; Bridge Constructions; Concrete Damages; Damage Mechanisms; Structural Health Monitoring; Tillståndsbedömning; Övervakning med sensorteknik; Fiberoptiska sensorer; Akustisk emission; Brokonstruktioner; Betongskador; Skademekanismer;

    Sammanfattning : Sverige har som många länder runt om i världen en åldrande infrastruktur och behovet av underhåll stiger. I en artikel i Svenska Dagbladet från 21/9–2018 redovisar analys- och teknikkonsultföretaget WSP en grov uppskattning att 300 miljarder kronor behöver investeras för att rusta upp existerande infrastruktur. LÄS MER

  4. 3. Quantifying uncertainty in structural condition with Bayesian deep learning : A study on the Z-24 bridge benchmark

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :David Steinar Asgrimsson; [2019]
    Nyckelord :Bayesian Deep Learning; Autoencoders; Bridge Structural Health Monitoring; Bridge Damage Detection;

    Sammanfattning : A machine learning approach to damage detection is presented for a bridge structural health monitoring system, validated on the renowned Z-24 bridge benchmark dataset where a sensor instrumented, threespan bridge was realistically damaged in stages. A Bayesian autoencoder neural network is trained to reconstruct raw sensor data sequences, with uncertainty bounds in prediction. LÄS MER

  5. 4. Model-Free Damage Detection for a Small-Scale Steel Bridge

    Master-uppsats, KTH/Bro- och stålbyggnad

    Författare :Aaron Ruffels; [2018]
    Nyckelord :damage detection; structural health monitoring; machine learning; artificial neural network; model bridge;

    Sammanfattning : Around the world bridges are ageing. In Europe approximately two thirds of all railway bridges are over 50 years old. As these structures age, it becomes increasingly important that they are properly maintained. If damage remains undetected this can lead to premature replacement which can have major financial and environmental costs. LÄS MER

  7. 5. Structural Health Monitoring of Bridges using Machine Learning : The influence of Temperature on the health prediction

    Master-uppsats, KTH/Bro- och stålbyggnad

    Författare :Elisa Khouri Chalouhi; [2016]
    Nyckelord :Damage detection; Structural Health Monitoring; Machine learning; Artificial Neural Network; Railway bridge; Environmental conditions.;

    Sammanfattning : A method that uses machine learning to detect and localize damage in railway bridges under various environmental conditions is proposed and validated in this work. The developed algorithm uses vertical and lateral deck accelerations as damage- sensitive features. LÄS MER