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Open access Aug 2026

Experimental and sustainability-driven engineering assessment of recycled concrete aggregates: mechanical performance, durability, environmental and economic implications

With the increased demand for concrete in modern infrastructure, coupled with the depletion of natural aggregates and the rising volume of construction and demolition waste, there has been a growing need for sustainable construction materials. This study evaluates the mechanical performance, durability, environmental impacts, and economic feasibility of concrete incorporating recycled concrete aggregates (RCA) as a partial or total replacement for natural coarse aggregates. Five concrete mixtures were prepared with RCA replacement levels of 0%, 25%, 50%, 75%, and 100%, aiming for a compressive strength of 30 MPa. Experimental investigations were conducted on compressive and flexural strength, workability, water absorption, and rapid chloride permeability, while environmental and economic performance were assessed through life cycle assessment and cost analysis. Results showed that concrete with 50% RCA achieved compressive strengths of 29.3 MPa and 4.1 MPa and flexural strength of 4.1 MPa at 28 days, which can be considered acceptable structural performance. Workability decreased with increasing RCA content due to increased porosity and water absorption, but improved significantly with the addition of a superplasticizer and aggregate pre-treatment, increasing the slump from 83mm to 120mm. Full RCA replacement resulted in a reduction of CO2 emissions by 32%, embodied energy by 33%, and concrete production cost by USD 12.3/m3. Overall, RCA shows a good potential for sustainable and circular infrastructure development.

A. Sil, Archanaa Dongre, A. Madhukar et al. · 0 citations
Open access Jul 2026

Artificial Intelligence–Driven Predictive Systems for Civil Engineering: Advancing Smart Infrastructure and Structural Health Insights

These studies propose an artificial intelligence driven predictive monitoring framework of vibration based structural health assessment under real conditions including where the labelled damage data is unavailable to validate that the proposed framework allows for interpretable, scalable and data-driven predictive monitoring.

A. Sil, Suhasini Kulkarni, Awdhesh Kumar et al. · 0 citations

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