Marufii, A., Dzhusuev, U., Turdazhieva, E., Zhalaldinov, M., & Alieva, A.
(2025).
Calculation of strip foundations in complex conditions of its operation based on digital technologies.
Architectural Studies,
11(1),
9-21.
https://doi.org/10.56318/as/1.2025.09
Article
Calculation of strip foundations in complex conditions of its operation based on digital technologies
Abstract
The study aimed to develop a methodology for calculating strip foundations with due regard for difficult operating conditions. For this, the peculiarities of foundations on weak and subsiding soils were considered, the effect of incomplete contact with the foundation was investigated, as well as the influence of longitudinal forces arising from pretensioning of reinforcement and temperature changes. The calculation methodology was based on modelling the foundation as a finite beam resting on a two-parameter elastic foundation. The study analysed the effect of incomplete contact between the base and the foundation, which occurs in the case of localised dips or soil weakness, as well as longitudinal forces caused by external loads. A calculation program was developed for numerical modelling and implemented in Delphi. The study determined that the absence of full contact between the foundation and the substrate leads to stress redistribution, which can cause localised deformation concentrations. Longitudinal forces have different effects on the performance of the foundation: tensile reduce deflections and compressive – increase them. Analytical and numerical calculations have confirmed the need to incorporate these factors during design, as ignoring them can lead to significant deviations in the stress-strain state of the structure. The developed mathematical model incorporates these effects and identifies critical areas requiring adjustment of design parameters. The data obtained can be used in the design of strip foundations in difficult ground conditions, increasing their reliability and efficiency, as well as minimising the risk of cracking and uneven settlements. The proposed methodology can be used to calculate the foundations of buildings and structures operating in heterogeneous soils
Keywords
soil foundation model; Heaviside function; bending stiffness; generalised soil characteristics; bedding coefficient; elastic modulus; moment of inertia
Received 30.08.2024, Revised 01.12.2024, Accepted 25.02.2025
Retrieved from Vol. 11, No. 1, 2025
Suggested citation
https://doi.org/10.56318/as/1.2025.09
Pages 9-21
References
[1] Alabi, M. (2024). Foundation engineering: Advanced techniques for challenging soil conditions. Retrieved from https://www.researchgate.net/publication/385286076_Foundation_Engineering_Advanced_Techniques_for_Challenging_Soil_Conditions.
[2] Aminisharifabad, M., Yang, Q., & Wu, X. (2021). A deep learning-based reliability model for complex survival data. IEEE Transactions on Reliability, 70(1), 73-81. doi: 10.1109/tr.2020.3045144.
[3] Baida, D., Voitsehivskiy, O., Popov, V., & Kotenko, V. (2024). The shear capacity of the reinforced concrete bridge beams. Modern Technologies, Materials and Structures in Construction, 21(1), 6-13. doi: 10.31649/2311-1429-2024-1-6-13.
[4] Chen, S., Feng, D., Wang, W., & Taciroglu, E. (2022). Probabilistic machine-learning methods for performance prediction of structure and infrastructures through natural gradient boosting. Journal of Structural Engineering, 148(8), article number 3401. doi: 10.1061/(asce)st.1943-541x.0003401.
[5] Clement, M. (2025). Scalability and efficiency of foundation models for Big Data analytics. Retrieved from https://www.researchgate.net/publication/388382655_Scalability_and_Efficiency_of_Foundation_Models_for_Big_Data_Analytics.
[6] Dhadse, G.D., Ramtekkar, G.D., & Bhatt, G. (2021). Finite element modeling of soil structure interaction system with interface: A review. Archives of Computational Methods in Engineering, 28(5), 3415-3432. doi: 10.1007/s11831-020-09505-2.
[7] Du, Y., Sheng, Q., Fu, X., Chen, H., & Li, G. (2022). New model for predicting the bearing capacity of large strip foundations on soil under combined loading. International Journal of Geomechanics, 22(5), article number 2389. doi: 10.1061/(asce)gm.1943-5622.0002389.
[8] Ertz, M., Latrous, I., Dakhlaoui, A., & Sun, S. (2024). The impact of Big Data Analytics on firm sustainable performance. Corporate Social Responsibility and Environmental Management, 32(1), 1261-1278. doi: 10.1002/csr.2990.
[9] Fissha, Y., Khatti, J., & Armaghani, D.J. (2024). Special issue: Advancement of computational mechanics in geotechnical engineering. Retrieved from https://www.researchgate.net/publication/380825985_Special_Issue_Advancement_of_Computational_Mechanics_in_Geotechnical_Engineering.
[10] Gao, X.-W., Jiang, W.-W., Xu, X.-B., Liu, H.-Y., Yang, K., Lv, J., & Cui, M. (2023). Overview of advanced numerical methods classified by operation dimensions. Aerospace Research Communications, 1, article number 11522. doi: 10.3389/arc.2023.11522.
[11] Hoshyar, A.N., Samali, B., Liyanapathirana, R., Houshyar, A.N., & Yu, Y. (2019). Structural damage detection and localization using a hybrid method and artificial intelligence techniques. Structural Health Monitoring, 19(5), 1507-1523. doi: 10.1177/1475921719887768.
[12] Jürgens, H., & Henke, S. (2021). The design of geotechnical structures using numerical methods. IOP Conference Series Earth and Environmental Science, 727(1), article number 012021. doi: 10.1088/1755-1315/727/1/012021.
[13] Liu, Q. (2023). Comparisons of conventional computing and quantum computing approaches. Highlights in Science Engineering and Technology, 38, 502-507. doi: 10.54097/hset.v38i.5875.
[14] Marufiy, A.T., & Kalykov, A.S. (2019). Creation of topologies of buildings and structures and methods for their effective processing at the design stage. Herald of KSUCTA, 65(3), 396-403.
[15] Marufiy, A.T., Tsoi, A.V., & Kalykov, A.S. (2021). Procedure for calculating the plate on an elastic base with a lot of reduced base rigidity. Science, New Technologies and Innovations of Kyrgyzstan, 1, 9-13.
[16] Messaouda, B., Assia, A., Salima, B., Nacera, K., & Lazhar, B. (2023). Numerical modeling of the behavior of a surface foundation located in the proximity of a slope. Soils and Rocks, 47(1), article number e2024008722. doi: 10.28927/sr.2024.008722.
[17] Onyelowe, K.C., et al. (2022). Selected AI optimization techniques and applications in geotechnical engineering. Cogent Engineering, 10(1), article number 2153419. doi: 10.1080/23311916.2022.2153419.
[18] Ramos, A., Correia, A.G., Nasrollahi, K., Nielsen, J.C., & Calçada, R. (2024). Machine learning models for predicting permanent deformation in railway tracks. Transportation Geotechnics, 47, article number 101289. doi: 10.1016/j.trgeo.2024.101289.
[19] Sadegh Es-haghi, M., Abbaspour, M., Abbasianjahromi, H., & Mariani, S. (2021). Machine learning-based prediction of the seismic bearing capacity of a shallow strip footing over a void in heterogeneous soils. Algorithms, 14(10), article number 288. doi: 10.3390/a14100288.
[20] Sasane, S., & Mulla, Z.A.S. (2024). Predictive modelling of stress levels: A comparative analysis of machine learning algorithms. Journal of Advanced Zoology, 45(S4), 153-158. doi: 10.53555/jaz.v45is4.4172.
[21] Savvides, A.A., & Papadopoulos, L. (2024). A neural network approach for the reliability analysis on failure of shallow foundations on cohesive soils. International Journal of Geo-Engineering, 15, article number 15. doi: 10.1186/s40703-024-00217-1.
[22] Schweiger, H.F., Fabris, C., Ausweger, G., & Hauser, L. (2018). Examples of successful numerical modelling of complex geotechnical problems. Innovative Infrastructure Solutions, 4, article number 2. doi: 10.1007/s41062-018-0189-5.
[23] Sharma, H., Patil, M., & Woolsey, C. (2020). A review of structure-preserving numerical methods for engineering applications. Computer Methods in Applied Mechanics and Engineering, 366, article number 113067. doi: 10.1016/j.cma.2020.113067.
[24] Yang, S., Yang, Z., Zhang, L., Guo, Y., Wang, J., & Huang, J. (2023). Research on deformation prediction of deep foundation pit excavation based on GWO-ELM model. Electronic Research Archive, 31(9), 5685-5700. doi: 10.3934/era.2023288.
[25] Zgoda, I. (2023). High performance modeling of the stress-strain state of thin-walled shell structures with the use of deep learning. Scientific and Technical Journal of Information Technologies Mechanics and Optics, 23(2), 430-435. doi: 10.17586/2226-1494-2023-23-2-430-435.
[26] Zhou, Z., Zhou, Z., & Vanapalli, S.K. (2024). Integrating analytical and machine learning approaches to simulate and predict dam foundation stress and river valley contraction in a large-scale reservoir. Bulletin of Engineering Geology and the Environment, 83, article number 444. doi: 10.1007/s10064-024-03941-1.