Calculation of strip foundations in complex conditions of its operation based on digital technologies

dc.citation.epage21
dc.citation.issue1
dc.citation.journalTitleАрхітектурні дослідження
dc.citation.spage9
dc.contributor.affiliationОшський технологічний університет імені М. М. Адишева
dc.contributor.affiliationОшський технологічний університет імені М. М. Адишева
dc.contributor.affiliationОшський технологічний університет імені М. М. Адишева
dc.contributor.affiliationОшський технологічний університет імені М. М. Адишева
dc.contributor.affiliationОшський технологічний університет імені М. М. Адишева
dc.contributor.affiliationOsh Technological University named after M.M. Adyshev
dc.contributor.affiliationOsh Technological University named after M.M. Adyshev
dc.contributor.affiliationOsh Technological University named after M.M. Adyshev
dc.contributor.affiliationOsh Technological University named after M.M. Adyshev
dc.contributor.affiliationOsh Technological University named after M.M. Adyshev
dc.contributor.authorМаруфій, Аділжан
dc.contributor.authorДжусуєв, Уметалі
dc.contributor.authorТурдажієва, Ельнура
dc.contributor.authorЖалалдінов, Муса
dc.contributor.authorАлієва, Анара
dc.contributor.authorMarufii, Adilzhan
dc.contributor.authorDzhusuev, Umetali
dc.contributor.authorTurdazhieva, Elnura
dc.contributor.authorZhalaldinov, Musa
dc.contributor.authorAlieva, Anara
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-11-24T12:02:59Z
dc.date.created2025-04-10
dc.date.issued2025-04-10
dc.description.abstractМетою дослідження була розробка методології розрахунку стрічкових фундаментів з урахуванням складних умов експлуатації. Для цього було розглянуто особливості фундаментів на слабких та просідаючих ґрунтах, досліджено вплив неповного контакту з фундаментом, а також вплив поздовжніх сил, що виникають внаслідок попереднього натягу арматури та зміни температури. Методологія розрахунку базувалася на моделюванні фундаменту як скінченної балки, що спирається на двопараметричний пружний фундамент. У дослідженні проаналізовано вплив неповного контакту між основою та фундаментом, що виникає у випадку локалізованих провалів або ослаблення ґрунту, а також поздовжніх сил, спричинених зовнішніми навантаженнями. Була розроблена розрахункова програма для числового моделювання та реалізована в Delphi. У дослідженні було визначено, що відсутність повного контакту між фундаментом та основою призводить до перерозподілу напружень, що може спричинити локалізовані концентрації деформацій. Поздовжні сили по-різному впливають на характеристики фундаменту: розтягуючі – зменшують прогини, а стискаючі – збільшують. Аналітичні та числові розрахунки підтвердили необхідність врахування цих факторів під час проектування, оскільки їх ігнорування може призвести до значних відхилень у напружено-деформованому стані конструкції. Розроблена математична модель враховує ці ефекти та визначає критичні області, що потребують коригування параметрів проектування. Отримані дані можуть бути використані при проектуванні стрічкових фундаментів у складних ґрунтових умовах, підвищуючи їх надійність та ефективність, а також мінімізуючи ризик утворення тріщин та нерівномірних осідань. Запропонована методологія може бути використана для розрахунку фундаментів будівель та споруд, що експлуатуються в неоднорідних ґрунтах
dc.description.abstractThe 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
dc.format.extent9-21
dc.format.pages13
dc.identifier.citationCalculation of strip foundations in complex conditions of its operation based on digital technologies / Adilzhan Marufii, Umetali Dzhusuev, Elnura Turdazhieva, Musa Zhalaldinov, Anara Alieva // Architectural Studies. — Lviv : Lviv Politechnic Publishing House, 2025. — Vol 11. — No 1. — P. 9–21.
dc.identifier.citation2015Calculation of strip foundations in complex conditions of its operation based on digital technologies / Marufii A. та ін. // Architectural Studies, Lviv. 2025. Vol 11. No 1. P. 9–21.
dc.identifier.citationenAPAMarufii, 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. Lviv Politechnic Publishing House..
dc.identifier.citationenCHICAGOMarufii 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 (Lviv), vol. 11, no 1, pp. 9-21.
dc.identifier.doi10.56318/as/1.2025.09
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/121569
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofАрхітектурні дослідження, 1 (11), 2025
dc.relation.ispartofArchitectural Studies, 1 (11), 2025
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dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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.
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dc.relation.references[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.
dc.relation.references[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.
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dc.relation.references[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.
dc.relation.references[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.
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dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.urihttps://www.researchgate.net/publication/385286076_Foundation_Engineering_Advanced_Techniques_for_Challenging_Soil_Conditions
dc.relation.urihttps://www.researchgate.net/publication/388382655_Scalability_and_Efficiency_of_Foundation_Models_for_Big_Data_Analytics
dc.relation.urihttps://www.researchgate.net/publication/380825985_Special_Issue_Advancement_of_Computational_Mechanics_in_Geotechnical_Engineering
dc.rights.holder© Національний університет „Львівська політехніка“, 2025
dc.subjectмодель ґрунтового фундаменту
dc.subjectфункція Хевісайда
dc.subjectжорсткість на згин
dc.subjectузагальнені характеристики ґрунту
dc.subjectкоефіцієнт нашарування
dc.subjectмодуль пружності
dc.subjectмомент інерції
dc.subjectsoil foundation model
dc.subjectHeaviside function
dc.subjectbending stiffness
dc.subjectgeneralised soil characteristics
dc.subjectbedding coefficient
dc.subjectelastic modulus
dc.subjectmoment of inertia
dc.subject.udc624.144.3
dc.subject.udc004.042.3
dc.titleCalculation of strip foundations in complex conditions of its operation based on digital technologies
dc.title.alternativeРозрахунок стрічкового фундаменту в складних умовах його експлуатації на основі цифрових технологій
dc.typeArticle

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