Abstract:An experimental study was conducted to evaluate the effect of compaction layer configuration, effort, and blow pattern on compressive strength and porosity characteristics of pervious concrete. Distinct types of compactions were applied to pervious concrete mixes with aggregate-to-cement (A/C) ratios ranging from 2.5 to 7.0. The results obtained from the experimental study revealed that three-layer compaction improved compressive resistance significantly compared with single-layer. In contrast, reduction in po… Show more
“…A prior study examined the A/C ratio's effect on performance and packing, which concluded that 2.65 was the theoretically ideal amount for aggregates of 12 to 18 mm [72]. An ideal A/C of roughly 4.0 was noted in another investigation [72]. This study uses a mixed design with A/C ranging in 0.5 increments from 3.0 to 5.0 to investigate this range and its possible impact on performance.…”
Section: Design and Specimen Preparationmentioning
confidence: 96%
“…An essential factor in pervious concrete is the A/C ratio, which usually ranges from 3.0 to 8.0 in the literature [71]. A prior study examined the A/C ratio's effect on performance and packing, which concluded that 2.65 was the theoretically ideal amount for aggregates of 12 to 18 mm [72]. An ideal A/C of roughly 4.0 was noted in another investigation [72].…”
Section: Design and Specimen Preparationmentioning
Ensuring quality in pervious concrete poses challenges, limiting its use. This work investigates the potential of machine learning to forecast its properties, offering a novel and accessible approach. Five machine learning techniques were employed on 300 experimental data points, considering mix parameters (aggregate size, ratio, compaction) and non-destructive measurement (ultrasonic velocity, resistivity). Artificial Neural Networks (ANNs) excelled, achieving high accuracy (R2 > 0.97) for prediction of porosity and compressive strength. Sensitivity analysis revealed the dominant influence of compaction energy, aggregate-to-cement ratio, and ultrasonic velocity, while aggregate size and resistivity had minimal impact. This study suggests that machine learning models, particularly ANNs, can be reliable and efficient for predicting pervious concrete properties. This has the potential to improve quality control and encourage broader adoption in the construction sector, ultimately leading to more sustainable and permeable infrastructure.
“…A prior study examined the A/C ratio's effect on performance and packing, which concluded that 2.65 was the theoretically ideal amount for aggregates of 12 to 18 mm [72]. An ideal A/C of roughly 4.0 was noted in another investigation [72]. This study uses a mixed design with A/C ranging in 0.5 increments from 3.0 to 5.0 to investigate this range and its possible impact on performance.…”
Section: Design and Specimen Preparationmentioning
confidence: 96%
“…An essential factor in pervious concrete is the A/C ratio, which usually ranges from 3.0 to 8.0 in the literature [71]. A prior study examined the A/C ratio's effect on performance and packing, which concluded that 2.65 was the theoretically ideal amount for aggregates of 12 to 18 mm [72]. An ideal A/C of roughly 4.0 was noted in another investigation [72].…”
Section: Design and Specimen Preparationmentioning
Ensuring quality in pervious concrete poses challenges, limiting its use. This work investigates the potential of machine learning to forecast its properties, offering a novel and accessible approach. Five machine learning techniques were employed on 300 experimental data points, considering mix parameters (aggregate size, ratio, compaction) and non-destructive measurement (ultrasonic velocity, resistivity). Artificial Neural Networks (ANNs) excelled, achieving high accuracy (R2 > 0.97) for prediction of porosity and compressive strength. Sensitivity analysis revealed the dominant influence of compaction energy, aggregate-to-cement ratio, and ultrasonic velocity, while aggregate size and resistivity had minimal impact. This study suggests that machine learning models, particularly ANNs, can be reliable and efficient for predicting pervious concrete properties. This has the potential to improve quality control and encourage broader adoption in the construction sector, ultimately leading to more sustainable and permeable infrastructure.
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