Over the last decades, numerous techniques have been proposed worldwide to assess the actual damage of critical concrete infrastructure. A method that has progressively been used in North America is a novel microscopic tool, the Damage Rating Index (DRI). This semi-quantitative petrographic tool was developed to reliably appraise both the nature and degree of damage in concrete affected by alkali-silica reaction (ASR), which may threaten the serviceability and the durability of concrete infrastructure around the world. Performing the DRI consists of counting numerous distress features (i.e. closed and open cracks in the aggregate and cement paste) encountered on the surface of polished concrete sections (lab-made specimens or cores extracted from field structures) using a stereomicroscope at 16x magnification; once recognized and counted, the distinct distress features are multiplied by weighting factors whose purpose is to balance their relative importance towards the distress mechanism under consideration (e.g., ASR). Although reliable and efficient, performing the DRI is exceptionally time-consuming, and its results are highly operator sensitive, requiring an experienced petrographer. Therefore, this study proposes using artificial intelligence (AI) through machine learning (ML) techniques to automate the DRI test protocol estimating the damage degree of concrete affected by ASR. The ML subfield known as Deep Learning (DL) was implemented to create human-like intelligence connections using a Convolutional Neural Network (CNN) algorithm, which can predict the DRI results (machine assessment) that are close to those expected (human assessment. This research is divided into two phases: 1) performing cracks recognition using sliding windows and 2) an advanced pixel recognition. In the first phase, the results displayed some inconsistencies in cracks classification; yet, for cracks identification in the cement paste, in particular, this method presented promising results. However, the advanced pixel recognition improved the drawbacks of the first phase, providing a more accurate cracks recognition and classification. The DRI number estimation was subsequently implemented into the CNN model achieving a 74.4% accuracy. Hence, the DRI automation is a revolutionary step towards a more ubiquitous use of the method since less time is required to perform the task, besides avoiding variability among petrographers and enabling non/less experienced professionals to take advantage of this powerful microscopic tool. With a more widely accessible diagnostic tool, ASR-affected critical concrete infrastructure could be more efficiently assessed, which would ultimately increase their safety.

Over the last decades, researchers have proposed a number of tools for the condition assessment of concrete infrastructure affected by alkali-silica reaction (ASR). Amongst those, increasing attention has been given to the Stiffness Damage Test (SDT), Damage Rating Index (DRI), and Residual Expansion (RE) laboratory test procedures that aim to determine the cause and extent (i.e., diagnosis) of damage along with the potential of further deterioration (i.e., prognosis) of affected concrete. Yet, most of the data gathered so far while using the aforementioned tools has been obtained on laboratory test specimens presenting distinct conditions from affected structural members in the field, especially regarding restraint effects. This work aims to understand the impact of restraint on ASR-induced expansion and damage. Thirty-two 450 mm by 450 mm by 675 mm concrete blocks with various reinforcement configurations (i.e., unreinforced, 1D and 2D reinforcement) and incorporating highly reactive coarse and fine aggregates (i.e., Springhill coarse and Texas sand) were manufactured and stored in conditions enabling ASR-induced development (i.e., 38°C and 100 R.H). Two expansion levels were selected for analysis (i.e., 0.08% and 0.15%); once reached, cores were extracted from three different directions (i.e., longitudinal, transversal and vertical) of all blocks and mechanical (i.e., SDT and compressive strength), microscopic (i.e., DRI, scanning electron microscope, etc.) and expansion (i.e., RE) test procedures were conducted on the concrete cores. Results suggest that the presence of restraint influences the induced expansion, resulting in an anisotropic response of the specimens. Furthermore, similar to the expansion behavior, an anisotropic distribution of induced damage and mechanical properties reduction are observed for the restrained concrete blocks in which the restraint configuration seems to significantly affect ASR-induced damage development and features. This led to the observation of a higher number of damage features, ASR development and mechanical properties reduction in cores obtained from unrestrained directions. Yet, some anticipated results from the current research will be studied in detail in the near future where the reliability of the existing techniques (i.e., residual expansion and soluble alkalis) for appraising ASR potential for further induced development and distress (i.e., prognosis) in affected concrete presenting distinct restraint scenarios will be evaluated.

Many highway and hydraulic structures in North America have been reported to be affected by alkali aggregate reaction (ASR). It is anticipated that most of these structures will be demolished as they approach the end of their service lives. Recycling demolished concrete as aggregates in new concrete is an option that not only reduces the amount of construction demolition waste (CDW) disposed in landfills but also lessens the consumption of non-renewable resources such as natural aggregates. However, the use of recycled concrete aggregate (RCA) in new concrete requires detailed research to make sure that the durability of the recycled material is not compromised, especially if the RCA had been previously affected by ASR. In this research project, coarse recycled concrete aggregate (RCA) is reclaimed and processed from distinct members (i.e. foundation blocks, bridge deck and columns) of an ASR-affected overpass after nearly 50 years of service. RCA concrete mixtures incorporating 50 and 100% replacement are manufactured and stored in conditions enabling further ASR development. Mechanical (i.e. Stiffness Damage Test - SDT) and microscopic (Damage Rating Index - DRI) analyses are performed at a fixed “secondary” induced expansion of 0.12%. Results show that the overall performance of the ASR-affected recycled mixtures depends upon the “past” condition of the RCA particles. Moreover, the DRI was able to capture the “past” and “secondary” induced expansion and damage of affected RCA while the SDT only detected the “secondary” distress development. Lastly, an adapted version of the DRI was proposed to further evaluate the overall damage of recycled concrete along with properly displaying “past” and “secondary” induced distress.

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Concrete has proven to be, by far, one of the most reliable materials for the construction of critical infrastructure. However, despite its structural capacity, concrete members are susceptible to damage mechanisms that may decrease its performance and durability throughout its service life. One such mechanism is alkali-silica reaction (ASR), which takes place when unstable siliceous phases present in coarse or fine aggregates react with the alkali hydroxides from the concrete pore solution, generating a secondary product (i.e., ASR gel); this product swells upon moisture uptake from the surrounding environment, leading to cracking and expansion of the affected concrete. In severe cases of ASR-affected infrastructure, structural safety could become a problem, and thus requiring the demolition of affected members. It is, therefore, necessary to adopt effective protocols for the diagnosis and prognosis of aging infrastructure, to ensure its performance over time along with properly planning for rehabilitation strategies, whether required. This work presents a two-stage case study of the S.I.T.E. building at the University of Ottawa for the diagnosis and prognosis of ASR-affected members (i.e., columns) after nearly 20 years in service. The diagnosis phase was conducted with the aim of evaluating the cause and extent of distress and interpreting its impact on the performance of the affected structure. First, a visual inspection was conducted to evaluate potentially damaged members, in order to select the best location for core-drilling. Once ASR was confirmed through petrographic examination, specimens were evaluated through the multi-level assessment (i.e., coupling of microscopic and mechanical assessment). A range of damage was discovered among the examined columns (i.e., 0.03%, 0.05%, and 0.08% expansion). Moreover, evidence of developing freeze and thaw (FT) damage was discovered in columns with greater levels of expansion, raising future concerns regarding the durability and serviceability of members affected by this coupling of damage (i.e., ASR+FT). For the second stage of this project (i.e., prognosis), a novel ASR semi-empirical model was developed with the aim of predicting future ASR-induced expansion and damage in the S.I.T.E. building. The above model was developed and validated (using ASR exposure site data) through the coupling of existing chemo-mechanical macro-models, which were used to predict material behaviour on the structural scale, and novel mathematical relationships for the prediction of anisotropy in the columns. Moreover, the use of the multi-level assessment to predict the mechanical implications of predicted distress was found to enhance the model’s capacity for prognosis and demonstrated important potential for the accurate prediction of multi-level damage in the S.I.T.E. columns.

Concrete is the most widely used construction material worldwide, yet, it presents major sustainability drawbacks due to the CO2 released during the manufacturing of its main constituent, cement. Several approaches are used to improve concrete’s eco-efficiency and reduce the binder intensity index, a metric used to measure the eco-efficiency of concrete, to a value below that of conventional concrete mixtures (i.e., 10 kg/m3.MPa-1 for 25-40 MPa mixtures). Particle Packing Models (PPM) is consequently an approach that can be used to enhance system packing density, reducing cement content while increasing hardened state properties and durability (i.e., reducing porosity). However, packed mixtures normally present issues in the fresh state while their hardened state performance is not fully comprehended. Therefore, this Ph.D. project proposes a new mix-design method called PPM-MP approach to develop eco-efficient mixtures. First, a detailed laboratory investigation was conducted on mixtures developed using the proposed approach in order to understand their fresh and hardened state performance. Concrete samples containing distinct ranges of cement content (320, 250, 200, 150 kg/m3) and slump (180, 90, and 20 +/- 20 mm) were fabricated and a wide range of fresh state tests (pH, temperature, fresh density, air content, slump and rheology over time) and hardened state tests (apparent porosity, surface electrical resistivity, compressive strength, and modulus of elasticity) were performed over time. Then, its performance against the alkali-silica reaction (ASR) induced expansion and deterioration, which is one of the leading and most damaging distress mechanisms issues in durability, was evaluated. In this section of the project, four sustainable concrete mixtures developed with varying cement content (e.g., 325, 250, 200, and 150 kg/m3) were developed and compared to a control mixture containing 420 kg/m3 of cement content. The mixtures were tested over a year under Concrete prism test (CPT) setup, which is the current method used to evaluate concrete ASR and using three different non-boosted test setups (i.e., Wrapped - W, Soaked - S, and Encapsulated - E). Moreover, two distinct types of highly reactive aggregates (e.g., Springhill Greywacke coarse aggregate and Texas Polymictic sand) were selected. Microscopic analysis was used to better understand the impact of ASR on sustainable mixtures, as well as the differences in ASR-damage and crack propagation under different test protocols. The results show the feasibility of producing an eco-efficient mixture in a more efficient manner which may contribute to the Net Zero Concrete targets. The proposed PPM-MP approach improves the sustainability of concrete mixtures and can be used for specific projects requiring 28-day compressive strength ranging from 18 to 45 MPa and slumps (180, 90, and 20 +/- 20 mm).