Modelling the Compressive Strength of Bituminous Sand Concrete

John Ebipuakebina Agori; Solomon Ugwa Okoroafor; Christopher Onome Aruawamedor; Jeff Oba; Lucky Osiwime Umukoro; Eseha Anthony Enifome; Moses Ogheneruemu Utoyo; Daniel Osakwe; Charles Iboyi

doi:doi:10.11648/j.ajce.20261405.20

Research Article |

| Peer-Reviewed

Modelling the Compressive Strength of Bituminous Sand Concrete

John Ebipuakebina Agori^*

, Solomon Ugwa Okoroafor

, Christopher Onome Aruawamedor

, Jeff Oba

, Lucky Osiwime Umukoro

, Eseha Anthony Enifome

, Moses Ogheneruemu Utoyo

, Daniel Osakwe

, Charles Iboyi

Published in American Journal of Civil Engineering (Volume 14, Issue 5)

Received: 2 October 2026 Accepted: 13 October 2026 Published: 29 October 2026

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

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 (D₁₀) 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/m³, 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/mm² at 28 days, target 38 N/mm²), 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/mm² at 7 days to 15.35 N/mm² at 150 days, against 15.17-26.93 N/mm² for the control (36-43% differences). BSC density (2213 kg/m³, 13.41 N/mm²) was within 10% of the control (2462 kg/m³, 22.32 N/mm²), 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 (R² = 0.8692) and the logarithmic model fitted BSC best (R² = 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 R² = 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.

Published in	American Journal of Civil Engineering (Volume 14, Issue 5)
DOI	10.11648/j.ajce.20261405.20
Page(s)	396-410
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Bituminous Sand, Bitumen Sand Concrete, Fine Aggregate, Compressive Strength, Regression Models, Machine Learning, Sustainable Concrete

References

[1]	Saha, A. K., Khan, Md. N. N., & Sarker, P. K. (2018). Value added utilization of by-product electric furnace ferronickel slag as construction materials: A review. Resources Conservation and Recycling, 134, 10-24. https://doi.org/10.1016/j.resconrec.2018.02.034
[2]	Perumal, P., Gouriou, C., Adesanya, E., Mistri, A., & Illikainen, M. (2024). Sustainable application of industrial side streams as alternative fine aggregates for cement mortar. Innovative Infrastructure Solutions, 9(1). https://doi.org/10.1007/s41062-023-01334-z
[3]	Bashir, M. T., Lal, R., Saifullah, H., Almutairi, A., Sikandar, M. A., Khan, Md. M. H., & Ounaies, W. (2026). A unified decision-oriented framework for sustainability assessment of electronic waste and recycled rubble aggregate concretes. Scientific Reports, 16(1). https://doi.org/10.1038/s41598-026-48896-3
[4]	Awad, M. T., Shaqadan, A., Al-Adwan, J., & Maraqa, F. (2023). Recycling of Basalt and Limestone Cutting Dust in Concrete Mix Design. Civil Engineering Journal, 9(5), 1173-1186. https://doi.org/10.28991/cej-2023-09-05-010
[5]	Dinh, H. L., Liu, J., Ong, D. E. L., & Doh, J. H. (2022). A sustainable solution to excessive river sand mining by utilizing by-products in concrete manufacturing: A state-of-the-art review. Cleaner Materials, 6, 100140. https://doi.org/10.1016/j.clema.2022.100140
[6]	Akhtar, M. N., Bani-Hani, K. A., Malkawi, D. A. H., & Albatayneh, O. (2024). Suitability of sustainable sand for concrete manufacturing - A complete review of recycled and desert sand substitution. Results in Engineering, 23, 102478. https://doi.org/10.1016/j.rineng.2024.102478
[7]	Sanusi, A., Ndububa, E. E., Amuda, A. G., Obianyo, I. I., Ahmed, A., Adeleke, W. A., Gunat, M. B., Abidemi, S. O., & Mambo, A. D. (2026). Replacing river sand in concrete: a review of emerging sustainable fine aggregate materials. Discover Sustainability, 7(1). https://doi.org/10.1007/s43621-026-02686-z
[8]	Impa, K. A., Sachin, K. C., Abhishek, R., Bekkeri, G. B., Shetty, K. K., & Shashikumara, S. R. (2024). Experimentation on triple-blended concrete with manufactured sand replaced by granulated blast furnace slag for fine aggregates. Innovative Infrastructure Solutions, 9(4). https://doi.org/10.1007/s41062-024-01409-5
[9]	Ezenkwa, C. S., Ede, A. N., Ogbaga, C. K., & Ogagaoghene, O. E. (2024). Utilization of Bituminous Sand in the Production of Eco-friendly Sandcrete for Sustainable Construction. IOP Conference Series Earth and Environmental Science, 1342(1), 12019. https://doi.org/10.1088/1755-1315/1342/1/012019
[10]	Ezenkwa, C. S., Onwuka, D. O., Okere, C. E., Arimanwa, J. I., & Onwuka, S. U. (2025). Compressive strength and statistical investigation of concrete made with bituminous sand. International Journal of Science and Research Archive, 14(3), 417-429. https://doi.org/10.30574/ijsra.2025.14.3.2431
[11]	Masi, G., Michelacci, A., Manzi, S., & Bignozzi, M. C. (2022). Assessment of reclaimed asphalt pavement as recycled aggregate for concrete. Archivio Istituzionale Della Ricerca, 341, 127745. https://doi.org/10.1016/j.conbuildmat.2022.127745
[12]	Masi, G., Michelacci, A., Manzi, S., Esposti, A. D., Pascale, B., Bònoli, A., & Bignozzi, M. C. (2023). Performance and environmental analysis of Reclaimed Asphalt Pavement concrete produced in industrial environment (pp. 2831-2838). https://doi.org/10.1201/9781003323020-344
[13]	Thomas, R. J., Fellows, A. J., & Sorensen, A. D. (2018). Durability Analysis of Recycled Asphalt Pavement as Partial Coarse Aggregate Replacement in a High-Strength Concrete Mixture. Journal of Materials in Civil Engineering, 30(5). https://doi.org/10.1061/(asce)mt.1943-5533.0002262
[14]	Abut, Y., & Yıldırım, S. T. (2019). An investigation on the durability properties of RAP-containing roller compacted concrete pavement. European Journal of Environmental and Civil Engineering, 26(2), 802-818. https://doi.org/10.1080/19648189.2019.1679672
[15]	Xu, W., Li, W., & Ji, Y. (2021). Mechanical Behavior Investigation of Reclaimed Asphalt Aggregate Concrete in a Cold Region. Materials, 14(15), 4101. https://doi.org/10.3390/ma14154101
[16]	Nehari, A., Amer, A. A. M., Mostefa, A. H., Safer, O., Salhi, M., Abdoun, M., & Nabil, G. (2024). The influence of fine recycled asphalt pavement aggregates on the properties of self-compacting mortar in fresh and hardened state. Studies in Engineering and Exact Sciences, 5(2). https://doi.org/10.54021/seesv5n2-440
[17]	Uzoka, C., & Imarhiagbe, A. (2025). Effect of Curing Duration and Methods on Concrete Compressive Strength of Concrete. Nile Journal of Engineering and Applied Science, 729-739. https://doi.org/10.5455/njeas.286654
[18]	Osadola, O. A., & Folagbade, S. O. (2026). Investigation of carbonation on the compressive strength development of Portland Limestone Cement concrete. Discover Civil Engineering, 3(1). https://doi.org/10.1007/s44290-026-00461-7
[19]	Lemon, Md. M. R. (2026). Experimental Investigation on the Durability Properties of Concrete under Aggressive Environmental Conditions. Journal of Ceramics and Concrete Sciences, 11(1), 46-70. https://doi.org/10.46610/joccs.2026.v011i01.005
[20]	Motlagh, S. A. T., & Naghizadehrokni, M. (2022). An extended multi-model regression approach for compressive strength prediction and optimization of a concrete mixture. Construction and Building Materials, 327, 126828. https://doi.org/10.1016/j.conbuildmat.2022.126828
[21]	Loureiro, A. A. B., & Stefani, R. (2024). Comparing the performance of machine learning models for predicting the compressive strength of concrete. Discover Civil Engineering, 1(1). https://doi.org/10.1007/s44290-024-00022-w
[22]	Sah, A. K., & Hong, Y.-M. (2024). Performance Comparison of Machine Learning Models for Concrete Compressive Strength Prediction. Materials, 17(9), 2075. https://doi.org/10.3390/ma17092075
[23]	Bonagura, M. (2012). Nondestructive evaluation of concrete compression strength by means of Artificial Neural Network [University of Bologna]. In AMS Dottorato Institutional Doctoral Theses Repository. https://doi.org/10.6092/unibo/amsdottorato/4880
[24]	Bonagura, M., & Nobile, L. (2021). Artificial Neural Network Approach for Predicting Concrete Compressive Strength by SonReb. Structural Durability & Health Monitoring, 15(2), 125-137. https://doi.org/10.32604/sdhm.2021.015644
[25]	Trtnik, G., Kavčič, F., & Turk, G. (2008). Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics, 49(1), 53-60. https://doi.org/10.1016/j.ultras.2008.05.001
[26]	Selçuk, S., & Tang, P. (2023). A metaheuristic-guided machine learning approach for concrete strength prediction with high mix design variability using ultrasonic pulse velocity data. Developments in the Built Environment, 15, 100220. https://doi.org/10.1016/j.dibe.2023.100220
[27]	Shaaban, M. M., Amin, M. A., Selim, S. Z., & Riad, I. M. (2025). Machine learning approaches for forecasting compressive strength of high-strength concrete. Scientific Reports, 15(1), 25567. https://doi.org/10.1038/s41598-025-10342-1
[28]	Elhishi, S., Elashry, A. M., & El‐Metwally, S. (2023). Unboxing machine learning models for concrete strength prediction using XAI. Scientific Reports, 13(1), 19892. https://doi.org/10.1038/s41598-023-47169-7
[29]	Akhtar, M. N., Husein Malkawi, D. A., & Albatayneh, O. (2026). Sustainable fine aggregate with SCMs-blends in structural concrete: A systematic research study. Results in Engineering, 32, 112265. https://doi.org/10.1016/j.rineng.2026.112265
[30]	Waliitagi, A., Rathanasalam, V. S., Kishore, H. R. B., & Jeeva Chithambaram, S. (2023). The implications of sustainable fine aggregate on self-compacting concrete: A review. IOP Conference Series: Materials Science and Engineering, 1273(1), 012007. https://doi.org/10.1088/1757-899X/1273/1/012007
[31]	Islam, Sk. R., Majumder, S. N., & Mutsuddy, R. (2024). Life cycle assessment and mechanical strength of cement composites with conventional, and recycled fine aggregate. Sustainable Structures, 4(2), 000052. https://doi.org/10.54113/j.sust.2024.000052
[32]	Anupam, K., Akinmade, D., Kasbergen, C., Erkens, S., & Adebiyi, F. (2023). A state-of-the-art review of natural bitumen in pavement: Underlining challenges and the way forward. Journal of Cleaner Production, 382, 134957. https://doi.org/10.1016/j.jclepro.2022.134957
[33]	Francis, D. O., Dayyabu, A., Aderinola, O. S., Kolo, M. U., Ijeoma, O. I., Akingbonmire, S. L., & Kolo, M. U. (2025). Potentials of bitumen tar sand for road work: A case study of Kajola, Lamudifa Irele LGA, Nigeria. Nile Journal of Engineering and Applied Science, 3(1). https://doi.org/10.5455/njeas.282386
[34]	Akpan, U. G., David, Z., & Emena, A. (2024). Comparative study on the extraction strength of solvents on tar sand from Okitipupa, Ondo State. Nigerian Journal of Technology, 43(1), 51-55. https://doi.org/10.4314/njt.v43i1.7
[35]	Olalekan, S. T., Arinkoola, A. O., Kolapo, S. K., Jimoh, M. O., Olufayo, A. O., Ayanshola, A. M., Ogunleye, O. O., & Abdulkareem, A. S. (2024). Durability of bitumen binder reinforced with polymer additives: Towards upgrading Nigerian local bitumen. Heliyon, 10(10), e30825. https://doi.org/10.1016/j.heliyon.2024.e30825
[36]	Brand, A. S., & Roesler, J. R. (2016). Reclaimed asphalt pavement in concrete pavements: Properties, microstructure, and design. In Proceedings of the 11th International Conference on Concrete Pavements (pp. 946-963). https://doi.org/10.33593/iccp.v11i1.335
[37]	Russo, N., Filippi, A., Carsana, M., Lollini, F., & Redaelli, E. (2025). Impact of RAP as recycled aggregate on durability-related parameters of structural concrete. Materials and Structures, 58, 53. https://doi.org/10.1617/s11527-025-02582-4
[38]	Uygunoğlu, T., Topçu, İ. B., & Çınar, E. (2024). Durability effect of reclaimed asphalt aggregate on concrete road pavement. Materiales de Construcción, 74(353). https://doi.org/10.3989/mc.2024.356823
[39]	Tiza, M. T., Agunwamba, J., & Okafor, F. (2024). Appraisal of reclaimed asphalt pavement as coarse aggregates in cement concrete. Environmental Research and Technology, 7(1), 27-40. https://doi.org/10.35208/ert.1320693
[40]	Sanchaiwut, M., Thumrongvut, J., Pantawee, S., & Seangatith, S. (2022). Effects of substitution of fine aggregate with recycled asphalt pavement on compressive strength of normal concrete. Key Engineering Materials, 922, 163-168. https://doi.org/10.4028/www.scientific.net/KEM.922.163
[41]	Chennuru, B., Deevi, V. B., Nallapati, J. N., Korampalli, R. H., Mallelli, P. V. B., Narendra Babu, P., & Kopuri, N. A. G. K. (2024). Experimental study on concrete by partial replacement of fine aggregate with bituminous road waste. International Journal of Engineering Research & Technology, 13(3). https://doi.org/10.17577/IJERTV13IS030020
[42]	Anwar, A., Tariq, H., Adil, S., & Iftikhar, M. A. (2022). Effect of curing techniques on compressive strength of concrete. World Journal of Advanced Research and Reviews, 16(3), 694-710. https://doi.org/10.30574/wjarr.2022.16.3.1379
[43]	Wang, Y., Xiao, R., Lu, H., Hu, W., Jiang, X., & Huang, B. (2023). Effect of curing conditions on the strength and durability of air-entrained concrete with and without fly ash. Cleaner Materials, 7, 100170. https://doi.org/10.1016/j.clema.2023.100170
[44]	Ram, S., Dengri, A., & Kumar, R. (2024). Assessment of compressive strength in ordinary Portland cement concrete: A study of curing methods and duration. Evergreen, 11(2), 640-651. https://doi.org/10.5109/7183321
[45]	Ferreira, R. L. S., Medeiros, M., Pereira, J. E. S., Henriques, G. F., Tavares, J. C., Marvila, M. T., & de Azevedo, A. R. G. (2023). Effects of particle size distribution of standard sands on the physical-mechanical properties of mortars. Materials, 16(2), 844. https://doi.org/10.3390/ma16020844
[46]	Paudel, S., Pudasaini, A., Shrestha, R. K., & Kharel, E. (2023). Compressive strength of concrete material using machine learning techniques. Cleaner Engineering and Technology, 15, 100661. https://doi.org/10.1016/j.clet.2023.100661
[47]	Sivakarthi, G., Viknesh, S. R., & Ram, S. Y. (2025). AI-driven multi-model prediction of concrete compressive strength using ANN, XGBoost, and random forest.
[48]	Almutairi, A. (2025). Explainable machine learning-based prediction of compressive strength in sustainable recycled aggregate self-compacting concrete using SHAP analysis. Sustainability, 17(24), 11334. https://doi.org/10.3390/su172411334
[49]	Ahmed, A. H. A., Jin, W., & Ali, M. A. H. (2024). Prediction of compressive strength of recycled concrete using gradient boosting models. Ain Shams Engineering Journal, 15(9), 102975. https://doi.org/10.1016/j.asej.2024.102975
[50]	Al Adnan, M., Babur, M., Farooq, F., Shahid, M., Ahmed, Z., & Das, P. (2025). Prediction of splitting tensile strength of fiber-reinforced recycled aggregate concrete utilizing machine learning models with SHAP analysis. Hybrid Advances, 11, 100507. https://doi.org/10.1016/j.hybadv.2025.100507
[51]	Zhang, Z., Luo, B., & Su, Y. (2026). Machine learning-based prediction of compressive strength in recycled aggregate self-compacting concrete: An ensemble modeling approach with SHAP interpretability analysis. Applied Sciences, 16(5), 2432. https://doi.org/10.3390/app16052432
[52]	Kim, J. (2025). Challenges with hard-to-learn data in developing machine learning models for predicting the strength of multi-recycled aggregate concrete. Applied Soft Computing, 113110. https://doi.org/10.1016/j.asoc.2025.113110
[53]	Padavala, S. S. A. B., Kothuri, D., & Gashe, A. M. (2026). Explainable machine learning-based optimization of strength, durability, and carbon efficiency of fly ash-GGBS geopolymer concrete. Scientific Reports, 16. https://doi.org/10.1038/s41598-026-48502-6
[54]	Pap, M., Mahler, A., Illés, Z., Nehme, S. G., & Biró, A. (2025). Comparative assessment of pore structure and water retention in concrete using MIP, CT, SEM, and dewpoint techniques. Periodica Polytechnica Civil Engineering, 69(4), 1203-1215. https://doi.org/10.3311/ppci.41892
[55]	Arora, S., Jangra, P., Lim, Y. Y., & Pham, T. M. (2023). Strength, durability, and microstructure of self-compacting geopolymer concrete produced with copper slag aggregates. Environmental Science and Pollution Research, 30, 666-684. https://doi.org/10.1007/s11356-022-22170-1
[56]	Bilgehan, M., & Turğut, P. (2010). Artificial neural network approach to predict compressive strength of concrete through ultrasonic pulse velocity. Research in Nondestructive Evaluation, 21(1), 1-17. https://doi.org/10.1080/09349840903122042
[57]	Collivignarelli, M. C., Cillari, G., Ricciardi, P., Miino, M. C., Torretta, V., Rada, E. C., & Abbà, A. (2020). The production of sustainable concrete with the use of alternative aggregates: A review. Sustainability, 12(19), 7903. https://doi.org/10.3390/su12197903
[58]	Berriache, A., Bensoula, M., Zaoui, M., & Kadri, T. (2023). Study and valorization of a new bituminous concrete based on natural dune sand for hot and arid environments. The Journal of Engineering and Exact Sciences, 9(12), 17791. https://doi.org/10.18540/jcecvl9iss12pp17791
[59]	Chou, J.-S., Chiu, C.-K., Farfoura, M., & Al-Taharwa, I. (2011). Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques. Journal of Computing in Civil Engineering, 25(3), 242-253. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000088

Cite This Article

Plain Text BibTeX RIS

APA Style

Agori, J. E., Okoroafor, S. U., Aruawamedor, C. O., Oba, J., Umukoro, L. O., et al. (2026). Modelling the Compressive Strength of Bituminous Sand Concrete. American Journal of Civil Engineering, 14(5), 396-410. https://doi.org/10.11648/j.ajce.20261405.20

Copy | Download

ACS Style

Agori, J. E.; Okoroafor, S. U.; Aruawamedor, C. O.; Oba, J.; Umukoro, L. O., et al. Modelling the Compressive Strength of Bituminous Sand Concrete. Am. J. Civ. Eng. 2026, 14(5), 396-410. doi: 10.11648/j.ajce.20261405.20

Copy | Download

AMA Style

Agori JE, Okoroafor SU, Aruawamedor CO, Oba J, Umukoro LO, et al. Modelling the Compressive Strength of Bituminous Sand Concrete. Am J Civ Eng. 2026;14(5):396-410. doi: 10.11648/j.ajce.20261405.20

Copy | Download

@article{10.11648/j.ajce.20261405.20,
  author = {John Ebipuakebina Agori and Solomon Ugwa Okoroafor and Christopher Onome Aruawamedor and Jeff Oba and Lucky Osiwime Umukoro and Eseha Anthony Enifome and Moses Ogheneruemu Utoyo and Daniel Osakwe and Charles Iboyi},
  title = {Modelling the Compressive Strength of Bituminous Sand Concrete},
  journal = {American Journal of Civil Engineering},
  volume = {14},
  number = {5},
  pages = {396-410},
  doi = {10.11648/j.ajce.20261405.20},
  url = {https://doi.org/10.11648/j.ajce.20261405.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajce.20261405.20},
  abstract = {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 (D10) 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/m3, 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/mm2 at 28 days, target 38 N/mm2), 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/mm2 at 7 days to 15.35 N/mm2 at 150 days, against 15.17-26.93 N/mm2 for the control (36-43% differences). BSC density (2213 kg/m3, 13.41 N/mm2) was within 10% of the control (2462 kg/m3, 22.32 N/mm2), 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 (R2 = 0.8692) and the logarithmic model fitted BSC best (R2 = 0.8008), with adequacy confirmed by F-statistics of 187.68 and 757.64 and a t-test (2.14 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.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Modelling the Compressive Strength of Bituminous Sand Concrete
AU  - John Ebipuakebina Agori
AU  - Solomon Ugwa Okoroafor
AU  - Christopher Onome Aruawamedor
AU  - Jeff Oba
AU  - Lucky Osiwime Umukoro
AU  - Eseha Anthony Enifome
AU  - Moses Ogheneruemu Utoyo
AU  - Daniel Osakwe
AU  - Charles Iboyi
Y1  - 2026/10/29
PY  - 2026
N1  - https://doi.org/10.11648/j.ajce.20261405.20
DO  - 10.11648/j.ajce.20261405.20
T2  - American Journal of Civil Engineering
JF  - American Journal of Civil Engineering
JO  - American Journal of Civil Engineering
SP  - 396
EP  - 410
PB  - Science Publishing Group
SN  - 2330-8737
UR  - https://doi.org/10.11648/j.ajce.20261405.20
AB  - 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 (D10) 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/m3, 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/mm2 at 28 days, target 38 N/mm2), 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/mm2 at 7 days to 15.35 N/mm2 at 150 days, against 15.17-26.93 N/mm2 for the control (36-43% differences). BSC density (2213 kg/m3, 13.41 N/mm2) was within 10% of the control (2462 kg/m3, 22.32 N/mm2), 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 (R2 = 0.8692) and the logarithmic model fitted BSC best (R2 = 0.8008), with adequacy confirmed by F-statistics of 187.68 and 757.64 and a t-test (2.14 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.
VL  - 14
IS  - 5
ER  -

Copy | Download

Author Information

John Ebipuakebina Agori

Department of Civil Engineering, Delta State University, Abraka, Nigeria

Contact Email

http://orcid.org/0009-0005-0189-0433
Solomon Ugwa Okoroafor

Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria

http://orcid.org/0000-0003-1538-4783
Christopher Onome Aruawamedor

Department of Civil Engineering, Delta State University, Abraka, Nigeria

http://orcid.org/0009-0009-7803-4319
Jeff Oba

Department of Civil Engineering, Federal University of Technology, Owerri, Nigeria

http://orcid.org/0009-0007-4804-1193
Lucky Osiwime Umukoro

Department of Civil Engineering, Delta State University, Abraka, Nigeria

http://orcid.org/0009-0002-9126-1574
Eseha Anthony Enifome

Department of Civil Engineering, Federal University of Technology, Owerri, Nigeria

http://orcid.org/0009-0007-9255-6821
Moses Ogheneruemu Utoyo

Department of Civil Engineering, Delta State University, Abraka, Nigeria

http://orcid.org/0009-0005-4260-9257
Daniel Osakwe

Department of Civil Engineering, Delta State University, Abraka, Nigeria

http://orcid.org/0009-0001-3832-1366
Charles Iboyi

Department of Civil Engineering, Delta State University, Abraka, Nigeria

http://orcid.org/0009-0004-1081-6780

Download PDF

Submit an Article

Sections

Plain Text BibTeX RIS

APA Style

Agori, J. E., Okoroafor, S. U., Aruawamedor, C. O., Oba, J., Umukoro, L. O., et al. (2026). Modelling the Compressive Strength of Bituminous Sand Concrete. American Journal of Civil Engineering, 14(5), 396-410. https://doi.org/10.11648/j.ajce.20261405.20

Copy | Download

ACS Style

Agori, J. E.; Okoroafor, S. U.; Aruawamedor, C. O.; Oba, J.; Umukoro, L. O., et al. Modelling the Compressive Strength of Bituminous Sand Concrete. Am. J. Civ. Eng. 2026, 14(5), 396-410. doi: 10.11648/j.ajce.20261405.20

Copy | Download

AMA Style

Agori JE, Okoroafor SU, Aruawamedor CO, Oba J, Umukoro LO, et al. Modelling the Compressive Strength of Bituminous Sand Concrete. Am J Civ Eng. 2026;14(5):396-410. doi: 10.11648/j.ajce.20261405.20

Copy | Download

@article{10.11648/j.ajce.20261405.20,
  author = {John Ebipuakebina Agori and Solomon Ugwa Okoroafor and Christopher Onome Aruawamedor and Jeff Oba and Lucky Osiwime Umukoro and Eseha Anthony Enifome and Moses Ogheneruemu Utoyo and Daniel Osakwe and Charles Iboyi},
  title = {Modelling the Compressive Strength of Bituminous Sand Concrete},
  journal = {American Journal of Civil Engineering},
  volume = {14},
  number = {5},
  pages = {396-410},
  doi = {10.11648/j.ajce.20261405.20},
  url = {https://doi.org/10.11648/j.ajce.20261405.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajce.20261405.20},
  abstract = {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 (D10) 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/m3, 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/mm2 at 28 days, target 38 N/mm2), 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/mm2 at 7 days to 15.35 N/mm2 at 150 days, against 15.17-26.93 N/mm2 for the control (36-43% differences). BSC density (2213 kg/m3, 13.41 N/mm2) was within 10% of the control (2462 kg/m3, 22.32 N/mm2), 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 (R2 = 0.8692) and the logarithmic model fitted BSC best (R2 = 0.8008), with adequacy confirmed by F-statistics of 187.68 and 757.64 and a t-test (2.14 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.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Modelling the Compressive Strength of Bituminous Sand Concrete
AU  - John Ebipuakebina Agori
AU  - Solomon Ugwa Okoroafor
AU  - Christopher Onome Aruawamedor
AU  - Jeff Oba
AU  - Lucky Osiwime Umukoro
AU  - Eseha Anthony Enifome
AU  - Moses Ogheneruemu Utoyo
AU  - Daniel Osakwe
AU  - Charles Iboyi
Y1  - 2026/10/29
PY  - 2026
N1  - https://doi.org/10.11648/j.ajce.20261405.20
DO  - 10.11648/j.ajce.20261405.20
T2  - American Journal of Civil Engineering
JF  - American Journal of Civil Engineering
JO  - American Journal of Civil Engineering
SP  - 396
EP  - 410
PB  - Science Publishing Group
SN  - 2330-8737
UR  - https://doi.org/10.11648/j.ajce.20261405.20
AB  - 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 (D10) 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/m3, 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/mm2 at 28 days, target 38 N/mm2), 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/mm2 at 7 days to 15.35 N/mm2 at 150 days, against 15.17-26.93 N/mm2 for the control (36-43% differences). BSC density (2213 kg/m3, 13.41 N/mm2) was within 10% of the control (2462 kg/m3, 22.32 N/mm2), 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 (R2 = 0.8692) and the logarithmic model fitted BSC best (R2 = 0.8008), with adequacy confirmed by F-statistics of 187.68 and 757.64 and a t-test (2.14 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.
VL  - 14
IS  - 5
ER  -

Copy | Download