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.