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New Trends in Determining Soil Properties

Year 2021, Issue: 28, 998 - 1007, 30.11.2021
https://doi.org/10.31590/ejosat.1012397

Abstract

Determination of soil properties and explanation of soil behavior are the basis of geotechnical engineering. Theories on soil behavior are developed from the 1900s, and today, a new phase has been started with new generation methods. Soil behavior can be modeled under the most complex conditions with the new generation numerical methods. While the determination of soil properties and the explanation of soil behavior are generally used in experimental studies and empirical theories obtained from these studies, nowadays, expert systems such as artificial neural networks, fuzzy logic, modification of existing experimental equipment and development of traditional experimental methodologies are frequently used. In this study, it is aimed to introduce new generation methods for determining soil properties and explaining soil behavior in geotechnical engineering.

References

  • Venkatramaiah, C. (1995). Geotechnical engineering. New Age International.
  • Erol, A. O., & Çekinmez, Z. (2014). Geoteknik mühendisliğinde saha deneyleri. Yüksel Proje Yayınları.
  • Isik, F., & Ozden, G. (2013). Estimating compaction parameters of fine-and coarse-grained soils by means of artificial neural networks. Environmental earth sciences, 69(7), 2287-2297.
  • Momeni, E., Nazir, R., Armaghani, D. J., & Maizir, H. (2014). Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement, 57, 122-131.
  • Díaz, E., Brotons, V., & Tomás, R. (2018). Use of artificial neural networks to predict 3-D elastic settlement of foundations on soils with inclined bedrock. Soils and Foundations, 58(6), 1414-1422.
  • Tran, T. T., Han, S. R., & Kim, D. (2018). Effect of probabilistic variation in soil properties and profile of site response. Soils and Foundations, 58(6), 1339-1349.
  • Oztoprak, S., Sargin, S., Uyar, H. K., & Bozbey, I. (2018). Modeling of pressuremeter tests to characterize the sands. Geomechanics and Engineering, 14(6), 509-517.
  • Pham, B. T., Hoang, T. A., Nguyen, D. M., & Bui, D. T. (2018). Prediction of shear strength of soft soil using machine learning methods. Catena, 166, 181-191.
  • Reale, C., Gavin, K., Librić, L., & Jurić-Kaćunić, D. (2018). Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks. Advanced Engineering Informatics, 36, 207-215.
  • Sihag, P. (2018). Prediction of unsaturated hydraulic conductivity using fuzzy logic and artificial neural network. Modeling Earth Systems and Environment, 4(1), 189-198.
  • Azadmard, B., Mosaddeghi, M. R., Ayoubi, S., Chavoshi, E., & Raoof, M. (2020). Estimation of near-saturated soil hydraulic properties using hybrid genetic algorithm-artificial neural network. Ecohydrology & Hydrobiology, 20(3), 437-449.
  • Ingale, R., Patel, A., & Mandal, A. (2020). Numerical modelling of bender element test in soils. Measurement, 152, 107310.
  • Adab, H., Morbidelli, R., Saltalippi, C., Moradian, M., & Ghalhari, G. A. F. (2020). Machine learning to estimate surface soil moisture from remote sensing data. Water, 12(11), 3223.
  • Straż, G., & Borowiec, A. (2020). Estimating the Unit Weight of Local Organic Soils from Laboratory Tests Using Artificial Neural Networks. Applied Sciences, 10(7), 2261.
  • Pham, B. T., Qi, C., Ho, L. S., Nguyen-Thoi, T., Al-Ansari, N., Nguyen, M. D., ... & Prakash, I. (2020). A novel hybrid soft computing model using random forest and particle swarm optimization for estimation of undrained shear strength of soil. Sustainability, 12(6), 2218.
  • Kayabali, K., Akturk, O., Fener, M., Ozkeser, A., Ustun, A. B., Dikmen, O., ... & Asadi, R. (2016). Determination of Atterberg limits using newly devised mud press machine. Journal of African Earth Sciences, 116, 127-133.
  • Dantas, G. H. S., Furlan, A. P., Fabbri, G. T. P., & Suárez, D. A. A. (2016). On gyratory compaction of a clayey soil. EJGE, 21(17), 5725-5733.
  • Zhao, C., Koseki, J., & Sasaki, T. (2018). Image based local deformation measurement of saturated sand specimen in undrained cyclic triaxial tests. Soils and Foundations, 58(6), 1313-1325.
  • Lu, C., Lu, J., Zhang, Y., & Puckett, M. H. (2019). A convenient method to estimate soil hydraulic conductivity using electrical conductivity and soil compaction degree. Journal of Hydrology, 575, 211-220.
  • Alkayış M.H., Laboratuvar Ölçekli Santrifüj Deney Sitemi ile Zemin Parametrelerinin Tayini. (2019) Yüksek Lisans Tezi, Eskişehir Teknik Üniversitesi.
  • Kučerík, J., Svatoň, K., Malý, S., Brtnický, M., Doležalová‐Weismannová, H., Demyan, M. S., ... & Tokarski, D. (2020). Determination of soil properties using thermogravimetry under laboratory conditions. European Journal of Soil Science, 71(3), 415-419.
  • Lozovsky, I. N., Zhostkov, R. A., & Churkin, A. A. (2020). Numerical Simulation of Ultrasonic Pile Integrity Testing. Russian Journal of Nondestructive Testing, 56(1), 1-11.
  • Yildiz, A., Graf, F., Rickli, C., & Springman, S. M. (2018). Determination of the shearing behaviour of root-permeated soils with a large-scale direct shear apparatus. Catena, 166, 98-113.
  • Beyaz, T., KAYABALI, K., & SÖNMEZER, Y. B. (2021). Kumların sıvılaşmasında rölatif sıkılık ve kesme birim deformasyonu etkisinin incelenmesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 431-440.
  • Balci, M. C., Kayabali, K., & Asadi, R. (2018). Miniature Centrifuge Modeling for Conventional Consolidation Test. Geotechnical Testing Journal, 41(3), 590-600.
  • Kayabali, K., Asadi, R., Fener, M., Dikmen, O., Habibzadeh, F., & Aktürk, Ö. (2020). Estimation of the compaction characteristics of soils using the static compaction method. Bulletin of the Mineral Research and Exploration, 162(162), 75-82.
  • Priest, J. A., Druce, M., Roberts, J., Schultheiss, P., Nakatsuka, Y., & Suzuki, K. (2015). PCATS Triaxial: A new geotechnical apparatus for characterizing pressure cores from the Nankai Trough, Japan. Marine and Petroleum Geology, 66, 460-470.
  • Suwal, L. P., & Kuwano, R. (2018). Triaxial apparatus equipped with elastic waves and matric suction measurement techniques. Soils and Foundations, 58(6), 1553-1562.
  • Li, L., & Zhang, X. (2014). Development of a new high-suction tensiometer. In Soil behavior and Geomechanics (pp. 416-425).

Zemin Özelliklerinin Belirlenmesinde Yeni Trendler

Year 2021, Issue: 28, 998 - 1007, 30.11.2021
https://doi.org/10.31590/ejosat.1012397

Abstract

Zemin özelliklerinin belirlenmesi ve zemin davranışının açıklanması geoteknik mühendisliğinin temellerini oluşturmaktadır. Zemin davranışına ilişkin teoriler 1900’lü yıllarda geliştirilmeye başlanmış, günümüzde ise yeni nesil yöntemler ile yeni bir aşamaya geçilmiştir. Yeni nesil nümerik metotlar ile en karmaşık koşullar altında zemin davranışı modellenebilmektedir. Zemin özelliklerinin belirlenmesi ve zemin davranışının açıklanması genel olarak deneysel çalışmalar ile bu çalışmalardan elde edilen verilen ampirik teorilerde kullanılması olarak yürümekteyken, günümüzde yapay sinir ağları, bulanık mantık gibi uzman sistemler ile mevcut deneysel ekipmanların modifikasyonu ve geleneksel deney metodolojilerinin geliştirilmesi sıkça kullanılmaya başlanmıştır. Bu çalışmada geoteknik mühendisliğinde zemin özelliklerinin belirlenmesi ve zemin davranışının açıklanmasına yönelik yeni nesil yöntemlerin tanıtılması amaçlanmıştır.

References

  • Venkatramaiah, C. (1995). Geotechnical engineering. New Age International.
  • Erol, A. O., & Çekinmez, Z. (2014). Geoteknik mühendisliğinde saha deneyleri. Yüksel Proje Yayınları.
  • Isik, F., & Ozden, G. (2013). Estimating compaction parameters of fine-and coarse-grained soils by means of artificial neural networks. Environmental earth sciences, 69(7), 2287-2297.
  • Momeni, E., Nazir, R., Armaghani, D. J., & Maizir, H. (2014). Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement, 57, 122-131.
  • Díaz, E., Brotons, V., & Tomás, R. (2018). Use of artificial neural networks to predict 3-D elastic settlement of foundations on soils with inclined bedrock. Soils and Foundations, 58(6), 1414-1422.
  • Tran, T. T., Han, S. R., & Kim, D. (2018). Effect of probabilistic variation in soil properties and profile of site response. Soils and Foundations, 58(6), 1339-1349.
  • Oztoprak, S., Sargin, S., Uyar, H. K., & Bozbey, I. (2018). Modeling of pressuremeter tests to characterize the sands. Geomechanics and Engineering, 14(6), 509-517.
  • Pham, B. T., Hoang, T. A., Nguyen, D. M., & Bui, D. T. (2018). Prediction of shear strength of soft soil using machine learning methods. Catena, 166, 181-191.
  • Reale, C., Gavin, K., Librić, L., & Jurić-Kaćunić, D. (2018). Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks. Advanced Engineering Informatics, 36, 207-215.
  • Sihag, P. (2018). Prediction of unsaturated hydraulic conductivity using fuzzy logic and artificial neural network. Modeling Earth Systems and Environment, 4(1), 189-198.
  • Azadmard, B., Mosaddeghi, M. R., Ayoubi, S., Chavoshi, E., & Raoof, M. (2020). Estimation of near-saturated soil hydraulic properties using hybrid genetic algorithm-artificial neural network. Ecohydrology & Hydrobiology, 20(3), 437-449.
  • Ingale, R., Patel, A., & Mandal, A. (2020). Numerical modelling of bender element test in soils. Measurement, 152, 107310.
  • Adab, H., Morbidelli, R., Saltalippi, C., Moradian, M., & Ghalhari, G. A. F. (2020). Machine learning to estimate surface soil moisture from remote sensing data. Water, 12(11), 3223.
  • Straż, G., & Borowiec, A. (2020). Estimating the Unit Weight of Local Organic Soils from Laboratory Tests Using Artificial Neural Networks. Applied Sciences, 10(7), 2261.
  • Pham, B. T., Qi, C., Ho, L. S., Nguyen-Thoi, T., Al-Ansari, N., Nguyen, M. D., ... & Prakash, I. (2020). A novel hybrid soft computing model using random forest and particle swarm optimization for estimation of undrained shear strength of soil. Sustainability, 12(6), 2218.
  • Kayabali, K., Akturk, O., Fener, M., Ozkeser, A., Ustun, A. B., Dikmen, O., ... & Asadi, R. (2016). Determination of Atterberg limits using newly devised mud press machine. Journal of African Earth Sciences, 116, 127-133.
  • Dantas, G. H. S., Furlan, A. P., Fabbri, G. T. P., & Suárez, D. A. A. (2016). On gyratory compaction of a clayey soil. EJGE, 21(17), 5725-5733.
  • Zhao, C., Koseki, J., & Sasaki, T. (2018). Image based local deformation measurement of saturated sand specimen in undrained cyclic triaxial tests. Soils and Foundations, 58(6), 1313-1325.
  • Lu, C., Lu, J., Zhang, Y., & Puckett, M. H. (2019). A convenient method to estimate soil hydraulic conductivity using electrical conductivity and soil compaction degree. Journal of Hydrology, 575, 211-220.
  • Alkayış M.H., Laboratuvar Ölçekli Santrifüj Deney Sitemi ile Zemin Parametrelerinin Tayini. (2019) Yüksek Lisans Tezi, Eskişehir Teknik Üniversitesi.
  • Kučerík, J., Svatoň, K., Malý, S., Brtnický, M., Doležalová‐Weismannová, H., Demyan, M. S., ... & Tokarski, D. (2020). Determination of soil properties using thermogravimetry under laboratory conditions. European Journal of Soil Science, 71(3), 415-419.
  • Lozovsky, I. N., Zhostkov, R. A., & Churkin, A. A. (2020). Numerical Simulation of Ultrasonic Pile Integrity Testing. Russian Journal of Nondestructive Testing, 56(1), 1-11.
  • Yildiz, A., Graf, F., Rickli, C., & Springman, S. M. (2018). Determination of the shearing behaviour of root-permeated soils with a large-scale direct shear apparatus. Catena, 166, 98-113.
  • Beyaz, T., KAYABALI, K., & SÖNMEZER, Y. B. (2021). Kumların sıvılaşmasında rölatif sıkılık ve kesme birim deformasyonu etkisinin incelenmesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 431-440.
  • Balci, M. C., Kayabali, K., & Asadi, R. (2018). Miniature Centrifuge Modeling for Conventional Consolidation Test. Geotechnical Testing Journal, 41(3), 590-600.
  • Kayabali, K., Asadi, R., Fener, M., Dikmen, O., Habibzadeh, F., & Aktürk, Ö. (2020). Estimation of the compaction characteristics of soils using the static compaction method. Bulletin of the Mineral Research and Exploration, 162(162), 75-82.
  • Priest, J. A., Druce, M., Roberts, J., Schultheiss, P., Nakatsuka, Y., & Suzuki, K. (2015). PCATS Triaxial: A new geotechnical apparatus for characterizing pressure cores from the Nankai Trough, Japan. Marine and Petroleum Geology, 66, 460-470.
  • Suwal, L. P., & Kuwano, R. (2018). Triaxial apparatus equipped with elastic waves and matric suction measurement techniques. Soils and Foundations, 58(6), 1553-1562.
  • Li, L., & Zhang, X. (2014). Development of a new high-suction tensiometer. In Soil behavior and Geomechanics (pp. 416-425).
There are 29 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Asena Karslıoğlu 0000-0001-5178-4069

Ahmet Ali Mert This is me 0000-0001-9205-488X

Mehmet İnanç Onur 0000-0002-2421-4471

Publication Date November 30, 2021
Published in Issue Year 2021 Issue: 28

Cite

APA Karslıoğlu, A., Mert, A. A., & Onur, M. İ. (2021). Zemin Özelliklerinin Belirlenmesinde Yeni Trendler. Avrupa Bilim Ve Teknoloji Dergisi(28), 998-1007. https://doi.org/10.31590/ejosat.1012397