Experimental Study on the Impact of Soil Type Variations on Compressive Strength and Settlement Characteristics of Spread Footing Foundations
Article Sidebar
Main Article Content
Abstract
This research investigates the influence of soil type variations on the compressive strength and settlement behavior of spread footing foundations. Soil properties such as moisture content, dry density, void ratio, cohesion, and internal friction angle play a crucial role in determining how foundations respond to applied loads. Variations in these properties can lead to uneven settlements and structural instability, posing significant challenges in construction. The study aims to provide a comprehensive understanding of these interactions to enhance foundation design and prevent structural failures. We applied machine learning techniques for data analysis and visualized patterns using Power BI, enabling a detailed exploration of the relationships between soil characteristics, compressive strength, and settlement behavior. The results showed that soil cohesion and internal friction angle had the most significant impact on compressive strength, while moisture content and void ratio were key contributors to settlement behavior. The optimized model achieved high accuracy of 82% in classifying settlement levels, reinforcing the dataset's reliability. This research highlights the importance of thorough soil testing and data-driven modeling in foundation design. We recommend integrating predictive models into geotechnical practice to support safer, more resilient structures, especially in areas with diverse soil profiles. The findings provide a valuable tool for engineers to make informed decisions, reducing the risk of foundation failure and enhancing the long-term stability of infrastructure.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Chen, Qiming. (2007). An experimental study on characteristics and behavior of reinforced soil foundation. LSU Doctoral Dissertations. 3361. https://repository.lsu.edu/gradschool_dissertations/3361
Shirvani, R. A., & Shooshpasha, I. (2015). Experimental study on load-settlement behaviour of cement stabilised footing with different dimensions on sandy soil. Arabian Journal for Science and Engineering, 40, 397-406.
Hanna, A., & Soliman, S. (2017). Experimental investigation of foundation on collapsible soils. Journal of Geotechnical and Geoenvironmental Engineering, 143(11), 04017085.
Akbas, S. O., & Kulhawy, F. H. (2009). Axial compression of footings in cohesionless soils. I: Load-settlement behavior. Journal of geotechnical and geoenvironmental engineering, 135(11), 1562-1574.
Farouk, A., & Shahien, M. M. (2013). Ground improvement using soil–cement columns: Experimental investigation. Alexandria Engineering Journal, 52(4), 733-740.
Adeniji, m. (2024). Investigating The Influence Of Soil Properties On Foundation Settlement And Bearing Capacity (Doctoral Dissertation, Department Of Civil Engineering, Faculty Of Engineering And Technology, Ladoke Akintola University Of Technology Ogbomoso).
Ahmad, H. (2025). Numerical study of comparing skirt sandpile with deep cement pile to improve load‒settlement response of circular footing in layered soils: Centric and eccentric effects. Heliyon, 11(2).
Abu-Farsakh, M., Chen, Q., & Sharma, R. (2013). An experimental evaluation of the behavior of footings on geosynthetic-reinforced sand. Soils and Foundations, 53(2), 335-348.
Lee, K. M., & Manjunath, V. R. (2000). Experimental and numerical studies of geosynthetic-reinforced sand slopes loaded with a footing. Canadian Geotechnical Journal, 37(4), 828-842.
Nujid, M. M., Tholibon, D. A., & Mukhlisin, M. (2024). Geotechnical and structural assessment on estimated bearing capacity of strip footing resting on silty sand incorporating moisture content effect. Case Studies in Construction Materials, 20, e03106.
Rodrigues, R. A., Prado Soares, F. V., & Sanchez, M. (2021). Settlement of footings on compacted and natural collapsible soils upon loading and soaking. Journal of Geotechnical and Geoenvironmental Engineering, 147(4), 04021010.
Al-Subari, L., Hanafi, M., & Ekinci, A. (2020). Effect of geosynthetic reinforcement on the bearing capacity of strip footing on sandy soil. SN Applied Sciences, 2(9), 1484.
Ahmad, H., Mahboubi, A., & Noorzad, A. (2022). Experimental study and numerical analysis of the bearing capacity of strip footing improved by wraparound geogrid sheets. Arabian Journal of Geosciences, 15(18), 1487.
Al-Dabagh, A., & Al-Omari, A. (2024). The Role of Footing Reinforcement by Micropiles: an Experimental and Numerical Study. Transportation Infrastructure Geotechnology, 11(4), 2319-2347.
Bhardwaj, A., & Sharma, R. K. (2023). Experimental and Numerical Investigations on the Bearing Capacity of Footings on the Layered Soil. International Journal of Geosynthetics and Ground Engineering, 9(4), 35.
Ebong, O., Edet, A., Uwah, A., & Udoetor, N. (2024). Comprehensive Impact Assessment of Intrusion Detection and Mitigation Strategies Using Support Vector Machine Classification. Research Journal of Pure Science and Technology, 7(2), 50-69.
Uwah, A., & Edet, A. (2024). Customized Web Application for Addressing Language ModelMisalignment through Reinforcement Learning from HumanFeedback. World Journal of Innovation And Modern Technology, 8(1), 62-71.
Edet, A., Ekong, B., & Attih, I. (2024). Machine Learning Enabled System for Health Impact Assessment of Soft Drink Consumption Using Ensemble Learning Technique. International Journal Of Computer Science And Mathematical Theory, 10(1), 79-101.
Ekong, A., Silas, A., & Inyang, S. (2022). A Machine Learning Approach for Prediction of Students’ Admissibility for Post-Secondary Education using Artificial Neural Network. Int. J. Comput. Appl, 184, 44-49.
Inyang, S., & Umoren, I. (2023). Semantic-Based Natural Language Processing for Classification of Infectious Diseases Based on Ecological Factors. International Journal of Innovative Research in Sciences and Engineering.
Ekong, A. P., Etuk, A., Inyang, S., & Ekere-obong, M. (2023). Securing against zero-day attacks: a machine learning approach for classification and organizations’ perception of its impact. Journal of Information Systems and Informatics, 5(3), 1123-1140.
Ekong, A., Ekong B., and Edet A. (2022). Supervised machine learning model for effective classification of patients with covid-19 symptoms based on bayesian belief network. Researchers Journal of Science and Technology, 2(1), 27-33.
Ekong, B., Ekong, O., Silas, A., Edet, A. E., & William, B. (2023). Machine Learning Approach for Classification of Sickle Cell Anemia in Teenagers Based on Bayesian Network. Journal of Information Systems and Informatics, 5(4), 1793-1808.
Edet, A. E., & Ansa, G. O. (2023). Machine learning enabled system for intelligent classification of host-based intrusion severity. Global Journal of Engineering and Technology Advances, 16(03), 041-050.
Ebong, O., Edet, A., Uwah, A., & Udoetor, N. (2024). Comprehensive Impact Assessment of Intrusion Detection and Mitigation Strategies Using Support Vector Machine Classification. Research Journal of Pure Science and Technology, 7(2), 50-69.
Ekong, A., James, G., Ekpe, G., Edet, A., & Dominic, E. A. (2024). Model For The Classification Of Bladder State Based On Bayesian Network. International Journal of Engineering and Artificial Intelligence, 5(2), 33-47.
Ekong, B., Edet, A., Udonna, U., Uwah, A., & Udoetor, N. (2024). Machine Learning Model for Adverse Drug Reaction Detection Based on Naive Bayes and XGBoost Algorithm. British Journal of Computer, Networking and Information Technology, 7(2), 97-114.
Edet, A., Udonna, U., Attih, I., & Uwah, A. (2024). Security Framework for Detection of Denial of Service (DoS) Attack on Virtual Private Networks for Efficient Data Transmission. Research Journal of Pure Science and Technology, 7(1), 71-81.
Udoetor, N., Ansa, G., Ekong, A., & Edet, A. (2024). Intelligent System for Detection of Copyright-Protected Data for Enhanced Data Security. Technology, 7(4), 58-80.
Edet, A., Silas, A., Ekaetor, E., Ebong, O., Isaac, E., & Udoetor, N. (2024). Data-Driven Framework for Classification and Management of Start-Up Risk For High Investment Returns. Advanced Journal of Science, Technology and Engineering, 4(2), 81-102.
Edet, A., Inyang, S., Umoren, I., & Etuk, U. E. (2024). Machine Learning Approach for Classification of Cyber Threats Actors in Web Region. Journal of Technology and Informatics (JoTI), 6(1), 70-77.