Print Email Facebook Twitter Monitoring Flexural Fatigue Life of Asphalt Concrete by Piezoelectric Sensors Title Monitoring Flexural Fatigue Life of Asphalt Concrete by Piezoelectric Sensors: Strategy for fatigue life prediction and damage detection based on load history on any asphalt matrix Author Kazmi, Syed Baqir Ali (TU Delft Civil Engineering & Geosciences) Contributor Fotouhi, M. (mentor) Liu, X. (graduation committee) Naus, Robert (mentor) Ghaderiaram, A. (graduation committee) Li, Y. (graduation committee) Degree granting institution Delft University of Technology Programme Civil Engineering Date 2023-11-17 Abstract This dissertation endeavors to introduce a novel supervised Structural Health Monitoring (SHM) methodology for the detection of damage and the prediction of fatigue life in asphalt concrete materials. Grounded in the principles of S-N (strain-number of cycles until failure) curves, this research addresses the intricate task of proficiently monitoring asphalt pavements through the utilization of sensor data, thus optimizing maintenance schedules. The successful implementation of this methodology holds the promise of significant cost savings, primarily by facilitating timely inspections and judicious resource allocation. The comprehensive approach encompasses an extensive literature review, encompassing topics such as asphalt properties, fatigue life, and damage prediction models. It also involves the strategic deployment of piezoelectric sensors, with a specific emphasis on Lead Zirconate Titanate (PZT), as well as four-point bending (4PB) testing. This methodology is further enriched by the incorporation of supervised machine learning techniques for the precise prediction of strain levels, subsequently utilized in fatigue life prediction and damage modeling. Subject Structural Health Monitoring (SHM)Fatigue Life PredictionAsphalt ConcretePiezoelectric SensorsPZTFour point bending testDamage DetectionMachine Learning (ML)Supervised machine learning To reference this document use: http://resolver.tudelft.nl/uuid:cacfa569-2cfc-4bfe-ad3a-f8be0015ded6 Part of collection Student theses Document type master thesis Rights © 2023 Syed Baqir Ali Kazmi Files PDF MSc_Thesis_Baqir_10_.pdf 12.32 MB Close viewer /islandora/object/uuid:cacfa569-2cfc-4bfe-ad3a-f8be0015ded6/datastream/OBJ/view