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Abstract Decarbonizing concrete production is a critical step toward achieving carbon neutrality by 2050. This paper highlights the advancements in artificial intelligence-assisted design of low-carbon cost-effective concrete, focusing on integrating machine learning-based property prediction with multi-objective optimization. Data collection and processing techniques, such as automatic data extraction, artificial data generation, and anomaly detection, are first discussed to address the importance of dataset quality. Strategies that capture physicochemical information of ingredients, including by-product supplementary cementitious materials and recycled aggregates, are then examined to enhance model generalizability. Various machine learning models—from individual regression approaches to heterogeneous ensemble methods—are compared for their predictive accuracy and robustness. Methods for hyperparameter tuning, model evaluation, and interpretation to ensure reliable and interpretable predictions are reviewed. Design optimization approaches are then highlighted, ranging from grid/random searches to more sophisticated gradient-based and metaheuristic algorithms, aimed at minimizing carbon footprint, embodied energy, and cost. Future research avenues encompass (1) application-specific design frameworks that integrate critical objectives—mechanical performance, durability, fresh-state behavior, and time-dependent properties such as creep and shrinkage—tailored to specific structural and environmental requirements; (2) holistic design optimization that simultaneously refines mixture design and structural parameters; and (3) probabilistic approaches to systematically manage uncertainties in materials, structural performance, and loading conditions systematically. 

The occurrence of local buckling, an external anomaly in pipelines, significantly contributes to pipeline incidents, posing challenges in monitoring such localized anomalies, particularly during pipeline operations. This paper introduces an approach aimed at monitoring local buckling occurring in the compression bending area of pipeline sections. The proposed approach utilizes fiber Bragg gratings (FBGs) to facilitate real-time measurement of strain changes. Experimental tests were conducted on the steel pipe equipped with FBGs positioned near the top and bottom of the pipe, subjected to four-point loading test to generate bending and local buckling. The strain data obtained from FBGs enable effective detection and localization of bending and buckling deformations during the loading process. This research contributes to enhancing the capability to monitor external threats to pipelines, thereby fostering improved condition assessments and bolstering infrastructure resilience.