Low-carbon and low-cost optimization framework of concrete under chloride environments with text-enhanced deep learning

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel AI-driven optimization framework for low-carbon concrete that treats performance as constraints, offering a methodological advancement.

🏢実務担当者:Use the framework to optimize concrete mixes for cost and carbon savings while meeting durability standards; the AI models can be adapted to local materials.

🏛政策担当者:The 17-20% emissions reductions demonstrate actionable pathways for construction sector decarbonization, supporting regulatory targets.

ABSTRACT Existing low-carbon concrete optimization approaches typically treat performance indicators such as compressive strength and durability as optimization objectives. However, in engineering practice, these indicators are required only to meet design code specifications and service-life requirements rather than to be continuously improved. This study proposes a multi-objective optimization framework for concrete under chloride environments, in which compressive strength and chloride diffusivity are treated as constraints rather than optimization objectives, while carbon emissions and cost are minimized as the primary objectives. In this framework, deep neural network (DNN) was employed to predict the compressive strength of concrete. Given the complexity of factors influencing chloride diffusion in concrete, including material properties and environmental conditions that are difficult to quantify and often described in textual form, this study developed a text-enhanced deep learning model to accurately predict the chloride diffusivity. The results demonstrate that both models can effectively capture the nonlinear relationships between the input variables and compressive strength/chloride diffusivity. Subsequently, the constraint-handled non-dominated sorting genetic algorithm II (NSGA-II) and technique for order preference by similarity to ideal solution (TOPSIS) were adopted to obtain the optimal solution. The optimization results of the concrete with low-carbon raw materials indicate that reductions of 20.35% in carbon emissions and 12.18% in cost can be achieved. In the benchmark case based on pier concrete from the Hong Kong-Zhuhai-Macao Bridge, the optimized concrete mix can reduce carbon emissions and cost by 17.34% and 3.00%, respectively.

• openalex https://doi.org/10.1016/j.cscm.2026.e06268first seen 2026-06-25 04:45:37