Optimizing sustainable concrete compressive strength prediction: A new particle swarm optimization-based metaheuristic approach to neural network modeling for circular economy and disaster resilience - Ulucan, Muhammed; Gunduzalp, Emrullah; Yildirim, Guengoer; Alatas, Bilal; Alyamac, Kursat Esat (Ernst & Sohn, 2025)
This study aims to predict the final-age compressive strengths of sustainable concrete series produced using different aggregate types with high accuracy using an artificial intelligence model and to reduce environmental degradation and conserve rapidly decreasing natural resources within the scope of sustainable development and circular economy goals. For this purpose, 45 different sustainable concrete series containing all-natural, recycled, natural, and recycled concrete aggregates were produced and subjected to compressive strength tests at 7 and 28 days. In addition, this study also considers whether deep neural network models, which have gained popularity in recent years but have high time and resource costs, or shallow models are more suitable for determining the compressive strength values with high accuracy. For this purpose, a method is proposed that can automatically generate the optimal neural network model using a metaheuristic approach that eliminates the human factor. For this purpose, the proposed method was extensively compared with different classical machine learning algorithms. The proposed method predicted the 7 and 28-day compressive strength with a coefficient of determination of 0.999 and presented better compressive strength predictions than all other algorithms. Considering the number of buildings to be constructed after earthquakes, the widespread use of concrete, and the importance of compressive strength, the proposed method will likely provide significant gains within sustainable development, circular economy, and disaster risk reduction.
Keywords: construction and demolition waste; deep learning; hyperparameter optimization; recycled concrete aggregate