Ultra High Performance (UHP) cementitious binders are a class of cement-based materials with high strength and ductility, designed for use in precast bridge connections, bridge superstructures, high load-bearing structural members like columns, and in structural repair and strengthening. This dissertation aims to elucidate the chemo-mechanical relationships in complex UHP binders to facilitate better microstructure-based design of these materials and develop machine learning (ML) models to predict their scale-relevant properties from microstructural information.To establish the connection between micromechanical properties and constitutive materials, nanoindentation and scanning electron microscopy experiments are performed on several cementitious pastes. Following Bayesian statistical clustering, mixed reaction products with scattered nanomechanical properties are observed, attributable to the low degree of reaction of the constituent particles, enhanced particle packing, and very low water-to-binder ratio of UHP binders. Relating the phase chemistry to the micromechanical properties, the chemical intensity ratios of Ca/Si and Al/Si are found to be important parameters influencing the incorporation of Al into the C-S-H gel.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.

A comprehensive study was performed on non-proprietary ultra-high-performance concrete (UHPC) material and several design methods were suggested based on numerous experimental results. Several sets of compression tests, direct tensile tests, and flexural tests were performed on UHPC to provide a better understanding of the mechanisms involved in the mechanical behavior of the fiber reinforced material. In addition to compressive tests, flexural tests, based on ASTM C1609 and EN 14651, were performed. The effect of the strain rate on the UHPC material was also investigated through the high-speed tensile tests at different strain rates. Alongside the usual measurement tools such as linear variable differential transformers (LVDT) and clip gages, digital image correlation (DIC) method was also used to capture the full-range deformations in the samples and localized crack propagations. Analytical approaches were suggested, based on the experimental results of the current research and other research groups, to provide design solutions for different applications and design approaches for UHPC and hybrid reinforced concrete (HRC) sections. The suggested methods can be used both in the ultimate limit state (ULS) and the serviceability limit state (SLS) design methods. Closed form relationships, based on the non-linear design of reinforced concrete, were used in the calculation of the load-deflection response of UHPC. The procedures were used in obtaining material properties from the flexural data using procedures that are based on back-calculation of material properties from the experimental results. Model simulations were compared with other results available in the literature. Performance of flexural reinforced UHPC concrete beam sections tested under different types of loading was addressed using a combination of fibers and rebars. The same analytical approach was suggested for the fiber reinforced concrete (FRC) sections strengthened (rehabilitated) by fiber reinforced polymers (FRP) and textile reinforced concrete (TRC). The objective is to validate the proper design procedures for flexural members as well as connection elements. The proposed solutions can be used to reduce total reinforcement by means of increasing the ductility of the FRC, HRC, and UHPC members in order to meet the required flexural reinforcement, which in some cases leads to total elimination of rebars.