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This research shows that brackish water increases the unconfined compressive strength of swelling soil/sugarcane bagasse ash (SCBA)/lime blends. Therefore, brackish water may substitute tap water in soil stabilization. Sodium chloride (NaCl) has been used in lime-ashes-soil treatments. In northeast Brazil, swelling soils are usual and artesian wells sometimes provide brackish water containing NaCl. Northeast Brazil also has a strong sugar and ethanol industry producing sugarcane bagasse ash (SCBA) as a byproduct. Therefore, brackish water can be used in soil-SCBA-lime stabilization. Hence, this work aims to evaluate the use of brackish water as a substitute for tap water in swelling soil-SCBA-lime blends stabilization. Two series of unconfined compression tests were carried out: one with tap water and the other with brackish water. In each group, the lime content varied from 4% to 8%, and the dry density from 13 kN/m3 to 15 kN/m3. All tests were carried out with a swelling soil-SCBA proportion of 75/25 and a water content of 22%. Results have shown that increasing lime content or dry density or using brackish water allowed to increase unconfined compression strength of swelling soil-SCBA-lime blends. The porosity/volumetric content of lime index (η/Liv) was suitable to predict the unconfined compressive strength of swelling soil-SCBA-lime blends, no matter if tap or brackish water was used in the molding process. Thus, brackish can be a feasible substitute for tap water in swelling soil-SCBA-lime stabilization, increasing blends unconfined compression strength, and preserving tap water, a scarce asset in Northeast Brazil.

© The Author(s) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/., Introduction: We previously developed an artificial intelligence (AI) model for automatic coronary angiography (CAG) segmentation, using deep learning. To validate this approach, the model was applied to a new dataset and results are reported. Methods: Retrospective selection of patients undergoing CAG and percutaneous coronary intervention or invasive physiology assessment over a one month period from four centers. A single frame was selected from images containing a lesion with a 50-99% stenosis (visual estimation). Automatic Quantitative Coronary Analysis (QCA) was performed with a validated software. Images were then segmented by the AI model. Lesion diameters, area overlap [based on true positive (TP) and true negative (TN) pixels] and a global segmentation score (GSS - 0 -100 points) - previously developed and published - were measured. Results: 123 regions of interest from 117 images across 90 patients were included. There were no significant differences between lesion diameter, percentage diameter stenosis and distal border diameter between the original/segmented images. There was a statistically significant albeit minor difference [0,19 mm (0,09-0,28)] regarding proximal border diameter. Overlap accuracy ((TP + TN)/(TP + TN + FP + FN)), sensitivity (TP / (TP + FN)) and Dice Score (2TP / (2TP + FN + FP)) between original/segmented images was 99,9%, 95,1% and 94,8%, respectively. The GSS was 92 (87-96), similar to the previously obtained value in the training dataset. Conclusion: the AI model was capable of accurate CAG segmentation across multiple performance metrics, when applied to a multicentric validation dataset. This paves the way for future research on its clinical uses., Open access funding provided by FCT|FCCN (b-on). Cardiovascular Center of the University of Lisbon, INESC-ID / Instituto Superior Técnico, University of Lisbon.