DOI: 10.11648/j.ajce.20261405.20 ISSN: 2330-8737

Modelling the Compressive Strength of Bituminous Sand Concrete

John Agori, Solomon Okoroafor, Christopher Aruawamedor, Jeff Oba, Lucky Umukoro, Eseha Enifome, Moses Utoyo, Daniel Osakwe, Charles Iboyi
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Global demand for river sand now outstrips natural replenishment, driving the search for sustainable alternatives. This study characterizes bituminous sand from bitumen deposits in Ondo State, Nigeria, as a substitute for river sand in concrete and develops regression and machine-learning frameworks to predict compressive strength. Aggregate characterization recorded effective sizes (D 10 ) of 0.123 mm (river sand) and 0.099 mm (bituminous sand), with uniformity coefficients of 3.959 and 4.495, indicating well-graded materials. Moisture contents averaged 72%, 60% and 8% for river sand, bituminous sand and granite; silt contents averaged 2.46% and 4.98%, against an 8% permissible limit. Specific gravities were 2.64, 2.60 and 2.70, bulk densities 1780, 1717 and 1677 kg/m 3 , and water absorption 0.91%, 0.70% and 0.74% respectively. Using a grade M25 mix of ratio 1: 1: 2 (characteristic strength 25 N/mm 2 at 28 days, target 38 N/mm 2 ), BSC showed higher workability (slump 55 mm vs 30 mm, compacting factor 0.49 vs 0.46) and strength gain with curing age, from 9.38 N/mm 2 at 7 days to 15.35 N/mm 2 at 150 days, against 15.17-26.93 N/mm 2 for the control (36-43% differences). BSC density (2213 kg/m 3 , 13.41 N/mm 2 ) was within 10% of the control (2462 kg/m 3 , 22.32 N/mm 2 ), classifying BSC as normal-weight concrete, and water absorption stayed within the 10-15% band (averages 11.37% and 11.30%). Multi-model regression: the exponential curing-age model fitted the control best (R 2 = 0.8692) and the logarithmic model fitted BSC best (R 2 = 0.8008), with adequacy confirmed by F-statistics of 187.68 and 757.64 and a t-test (2.14 < 2.57). A five-stage machine-learning framework is then specified, benchmarked against ANN achieving R 2 = 0.894 with the lowest RMSE (5.192 MPa) on a 1234-record dataset. The results position bituminous sand as a technically viable, sustainable alternative fine aggregate with a data-driven strength-prediction route.

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