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<p>In this paper, the macroproperties of sustainable masonry mortar developed using autoclaved aerated concrete debris (AACD) as a partial fine aggregate replacement are examined, which is supported by artificial neural network (ANN) modeling. Mortar mixes were prepared with replacement levels of 0, 10, 20, 30, 40, and 50%, and all tests were conducted on three replicate samples per mix (<i>n</i> = 3). The fluidity, physical, and mechanical properties of the mortars were evaluated. AACD incorporation increased water demand, with fluidity values rising by approximately 5% relative to those of the control mix. Analysis of the water absorption capacity revealed that compared with the conventional reference samples, the AACD control mixture had 104.7% higher water absorption because the porous material mix had a higher absorptive capacity. The volume of voids ranged between 20 and 29.25% and 16.5% for the control mix, whereas the density decreased by 12.7% at higher replacement levels. Compared with that of the control sample, the compressive strength of the A30 mix improved to 31.4% greater. The flexural strength and ultrasonic pulse velocity (UPV) exhibited similar trends, indicating enhanced matrix densification. The ANN model was developed using the AAC replacement level and mixture parameters as input variables, with compressive strength, flexural strength, and UPV as the output responses, achieving strong predictive accuracy (R² = 0.94). These findings demonstrate the feasibility of incorporating AACD as a sustainable fine aggregate alternative in masonry mortar.</p>

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Experimental and ANN-based prediction of masonry mortar properties incorporating autoclaved aerated concrete debris

  • 期刊论文

  • 发表日期：

    2026年3月20日

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  • Vijay Murugan Sivaperumal,
  • M. Vishnupriyan,
  • K. Poongodi

摘要

In this paper, the macroproperties of sustainable masonry mortar developed using autoclaved aerated concrete debris (AACD) as a partial fine aggregate replacement are examined, which is supported by artificial neural network (ANN) modeling. Mortar mixes were prepared with replacement levels of 0, 10, 20, 30, 40, and 50%, and all tests were conducted on three replicate samples per mix (n = 3). The fluidity, physical, and mechanical properties of the mortars were evaluated. AACD incorporation increased water demand, with fluidity values rising by approximately 5% relative to those of the control mix. Analysis of the water absorption capacity revealed that compared with the conventional reference samples, the AACD control mixture had 104.7% higher water absorption because the porous material mix had a higher absorptive capacity. The volume of voids ranged between 20 and 29.25% and 16.5% for the control mix, whereas the density decreased by 12.7% at higher replacement levels. Compared with that of the control sample, the compressive strength of the A30 mix improved to 31.4% greater. The flexural strength and ultrasonic pulse velocity (UPV) exhibited similar trends, indicating enhanced matrix densification. The ANN model was developed using the AAC replacement level and mixture parameters as input variables, with compressive strength, flexural strength, and UPV as the output responses, achieving strong predictive accuracy (R² = 0.94). These findings demonstrate the feasibility of incorporating AACD as a sustainable fine aggregate alternative in masonry mortar.