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Wakjira, Tadesse Gemeda

University of British Columbia

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.

Text; Still Image

2024-04-10

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/87737

Civil Engineering

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/