Advanced AI-powered comprehensive framework for modelling, analysis, assessment, and performance-based design of ultra-high-performance concrete elements
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Wakjira, Tadesse Gemeda
issued: 2024-04-10T18:40:19Z
issued: 2024
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: This doctoral dissertation presents a novel framework for sustainable design, structural modelling, and performance-based design of ultra-high-performance concrete (UHPC) utilizing state-of-the-art artificial intelligence techniques. Initially, a novel framework for strength prediction and multi-objective optimization (MOO) of UHPC mixture was introduced using advanced machine learning (ML) and MOO algorithms. A total of 19 objective functions are considered, including cost, uniaxial compressive strength, and 17 environmental impact metrics that comprehensively evaluate the environmental sustainability of the UHPC mixture. Addressing the need for an accurate stress-strain constitutive model of confined UHPC, this dissertation introduces a hybrid ML model, augmented by a state-of-the-art conditional tabular generative adversarial network and Optuna to accurately predict the peak and ultimate stress-strain responses of UHPC confined with normal strength steel (NSS) or high-strength steel (HSS) reinforcement. Furthermore, hybrid ML-based predictive models for a complete stress-strain response curve of UHPC confined with NSS or HSS spirals are proposed. Besides, analytical equations are developed to predict the peak and ultimate stress-strain responses of confined UHPC. Utilizing these models and the uncertainties of the optimized UHPC along with other sources of uncertainties, drift capacity limit states are proposed for UHPC columns based on explainable ML models across four different damage states. The efficacy of the developed model is assessed using a range of statistical metrics, including the composite fitness score, which demonstrated a high predictive accuracy of the drift limit states. Moreover, the application of the proposed drift limit states in the performance-based design of UHPC columns is investigated. Lastly, the dissertation introduces a multivariate seismic fragility assessment approach for UHPC bridges using a hybrid ML model.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/87737
note: ADVANCED AI-POWERED COMPREHENSIVE FRAMEWORK FOR MODELLING, ANALYSIS, ASSESSMENT, AND PERFORMANCE-BASED DESIGN OF ULTRA-HIGH-PERFORMANCE CONCRETE ELEMENTS by Tadesse Gemeda Wakjira A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2024 © Tadesse Gemeda Wakjira, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the dissertation entitled: Advanced AI-Powered Comprehensive Framework for Modelling, Analysis, Assessment, and Performance-Based Design of Ultra-High-Performance Concrete Elements submitted by Tadesse Gemeda Wakjira in partial fulfillment of the requirements of the degree of Doctor of Philosophy. Dr. M. Shahria Alam, School of Engineering Supervisor Dr. Rehan Sadiq, School of Engineering Supervisory Committee Member Dr. Abbas Milani, School of Engineering Supervisory Committee Member Dr. Mohammad H. Zarifi, School of Engineering University Examiner Dr. Mohamed A. Moustafa, University of Nevada, Reno External Examiner iii ABSTRACT This doctoral dissertation presents a novel framework for sustainable design, structural modelling, and performance-based design of ultra-high-performance concrete (UHPC) utilizing state-of-the-art artificial intelligence techniques. Initially, a novel framework for strength prediction and multi-objective optimization (MOO) of UHPC mixture was introduced using advanced machine learning (ML) and MOO algorithms. A total of 19 objective functions are considered, including cost, uniaxial compressive strength, and 17 environmental impact metrics that comprehensively evaluate the environmental sustainability of the UHPC mixture. Addressing the need for an accurate stress-strain constitutive model of confined UHPC, this dissertation introduces a hybrid ML model, augmented by a state-of-the-art condition…
has type: Thesis/Dissertation
date issued: 2024-05
is shown at: 10.14288/1.0441282
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Alam, M. Shahria
type: Text
type: Still Image
identifier URI: http://hdl.handle.net/2429/87737