پیش‌بینی مقاومت فشاری و کششی بستر رسی تثبیت‌شده با سیمان و باطله سنگ‌آهن با استفاده از روش‌های هوش محاسباتی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی عمران، دانشگاه صنعتی سیرجان.

2 دانشیار، دانشکده مهندسی عمران، دانشگاه صنعتی سیرجان، سیرجان، ایران

10.22091/cer.2020.5950.1213

چکیده

یکی از روش‌های بهسازی خاک بستر روسازی، استفاده از مواد افزودنی جهت تثبیت خاک است. برای ارزیابی کیفیت مصالح از آزمایش‌های مقاومت فشاری تک‌محوری و مقاومت کششی غیرمستقیم استفاده می‌شود. بهره‌گیری از این آزمایش‌ها با توجه به زمان‌بر بودن عمل‌آوری نمونه‌ها وقت‌گیر است .همچنین در صورت افزایش تعداد نمونه‌ها می‌تواند هزینه‌بر نیز باشد. در این تحقیق، از دو روش مدل‌سازی شبکه عصبی مصنوعی (ANN) و سیستم استنتاج عصبی- فازی تطبیقی (ANFIS) جهت پیش‌بینی مقاومت فشاری و کششی خاک رس تثبیت‌شده با سیمان و باطله سنگ‌آهن استفاده شده است. برای این منظور، پارامترهای درصد سیمان، درصد باطله، رطوبت بهینه و مدت زمان عمل‌آوری، به عنوان ورودی و پارامترهای مقاومت فشاری تک‌محوری و مقاومت کششی غیرمستقیم، به عنوان خروجی در نظر گرفته شده است و در هر مورد پایگاه داده‌ای متشکل از 100 داده مورد استفاده قرار گرفته است. مدل‌سازی با استفاده از این سه روش، برتری مدل شبکه عصبی مصنوعی نسبت به سیستم استنتاج عصبی- فازی تطبیقی را نشان می‌دهد. همچنین تحلیل حساسیت نشان می‌دهد که پارامترهای درصد سیمان و درصد باطله به‌ترتیب بیشترین و کمترین تأثیر را بر مقاومت فشاری و مقاومت کششی پیش‌بینی شده دارند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Prediction of Compressive and Tensile Strength of Clayey Subgrade Soil Stabilized With Portland Cement and Iron Ore Mine Tailing Using Computational Intelligence Methods

نویسندگان [English]

  • Toba Heidari Dezfuli 1
  • Ali Reza Ghanizadeh 2
1 Department of Civil Engineering, Sirjan University of Technology
2 Associate Professor at Faculty of Civil and Environmental Engineering, Sirjan University of Technology, Sirjan, Iran.
چکیده [English]

One of the Practical solutions for improving subgrade soil is the utilization of additives for soil stabilization. Generally, the unconfined compressive strength (UCS) and indirect tensile strength (ITS) tests are employed for quality control of stabilized materials. These tests are time- consuming due to the time needs for curing of samples, and can also be costly if the number of samples increases. In this study, we have employed two methods including artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) to predict UCS and ITS of clayey subgrade soil stabilized with Portland cement and iron ore mine tailing (IOMT). To this end, cement content, IOMT content, optimum moisture, and curing time were considered as input parameters, and unconfined compressive strength, as well as indirect tensile strength, were considered as output parameters and in each case a dataset consisting of 100 data points were used for developing computational intelligence models. Modeling by means of these three methods confirmsthe superiority of the artificial neural network model over ANFIS model. Also, the sensitivity analysis showed that the Portland cement content and IOMT Content have the greatest and lowest effect on the predicted compressive and tensile strength, respectively.

کلیدواژه‌ها [English]

  • Unconfined Compressive Strength
  • Indirect Tensile Strength
  • clay Soil
  • Portland cement and iron ore mine tailings
  • computational intelligence
[1] Mu, T. (2013). Soil Stabilization with Fly Ash and Fibers. Thesis of Master of Science, Southern Illinois University Carbondale.
[2] Bergado, D. T., Anderson, L. R., Miura, N., & Balasubramaniam, A. S. (1996). Soft ground improvement in lowland and other environments. ASCE, 978-0-7844-0151-4 (ISBN-13)|0-7844-0151-9 (ISBN-10).
[3] Braja, M. D. (1990). Principle of foundation engineering. PWS-KENT, Boston, 978-0495668107 (ISBN-13)|0495668109 (ISBN-10).
[4] Nelson, J., & Miller, D. J. (1997). Expansive soils: problems and practice in foundation and pavement engi-neering. John Wiley & Sons.
[5] Barclay, R. T., Casias, T. J. E. A., David A, C., De Graffenreid, R. L., Hess, J. R., Roof, H. C., & Dennis, W. S. (1990). “State-of-the-art report on soil cement”, ACI Materials Journal, 87(4), 395-417.
[6] Ghasemi, M., & Nezamabadi, B. (2016). “Evaluating the effect of iron mine wastes on the compressive strength of lime concrete”, 3rd International Congress on New Research Achievements in Civil Engineering, Architecture & Urban Management, Tehran, Iran.
[7] Ghasemi, M., & Nezamabadi, B. (2016). “Evaluating the effect of iron mine wastes on the strength of ce-ment sand mortars”, 3rd International Congress on New Research Achievements in Civil Engineering, Archi-tecture & Urban Management, Tehran, Iran.
[8] Ghasemi, M., & Normandi, A. (2016). “Evaluating the effect of iron mine wastes on lime stabilized soil”, 1st International Comprehensive Competition Conference on Engineering Science in Iran, Anzali, Iran.
[9] Yarmahmoudi, A. (2018). Stabilization of cohesive red clay soil using Portland cement and iron ore mine tailing. M.Sc Thesis, Department of Civil Engineering, Sirjan University of Technology, Iran.
[10] Gunaydin, O., Gokoglu, A., & Fener, M. (2010). “Prediction of artificial soil’s unconfined compression strength test using statistical analyses and artificial neural networks”, Advances in Engineering Software, 41(9), 1115-1123.
[11] Alavi, A. H., & Gandomi, A. H. (2011). “A robust data mining approach for formulation of geotechnical engineering systems”, Engineering Computations, 28(3), 242-274.
[12] Das, S. K., Samui, P., & Sabat, A. K. (2011). “Application of Artificial Intelligence to Maximum Dry Densi-ty and Unconfined Compressive Strength of Cement Stabilized Soil”, Geotechnical and Geological Engineer-ing, 29(3), 329-342.
[13] Shrestha, R., & Al-Tabbaa, A. (2012). “Development of predictive models for cement stabilized soils”, In Grouting and Deep Mixing, 2012, 221-230.
[14] Motamedi, S., Shamshirband, S., Hashim, R., Petković, D., & Roy, C. (2015). “RETRACTED: Estimating unconfined compressive strength of cockle shell–cement–sand mixtures using soft computing methodologies”, Engineering Structures, 98, 49-58.
[15] Motamedi, S., Shamshirband, S., Petković, D., & Hashim, R. (2015). “Application of adaptive neuro-fuzzy technique to predict the unconfined compressive strength of PFA-sand-cement mixture”, Powder Technology, 278, 278-285.
[16] Suman, S., Mahamaya, M., & Das, S. K. (2016). “Prediction of Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilised Soil Using Artificial Intelligence Techniques”, International Journal of Geosynthetics and Ground Engineering, 2(2), 1-11.
[17] Mozumder, R. A., & Laskar, A. I. (2015). “Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using Artificial Neural Network”, Computers and Geotechnics, 69, 291-300.
[18] Mozumder, R. A., Laskar, A. I., & Hussain, M. (2017). “Empirical approach for strength prediction of geo-polymer stabilized clayey soil using support vector machines”, Construction and Building Materials, 132, 412-424.
[19] Javdanian, H. (2017). “The Effect of Geopolymerization on the Unconfined Compressive Strength of Sta-bilized Fine-grained Soils”, International Journal of Engineering, 30(11), 1673-1680.
[20] Sathyapriya, S., Arumairaj, P. D., & Ranjini, D. (2017). “Prediction of unconfined compressive strength of a stabilised expansive clay soil using ANN and regression analysis (SPSS)”, Asian Journal of Research in Social Sciences and Humanities, 7(2), 109-123.
[21] Güllü, H., & Fedakar, H. I. (2017). “On the prediction of unconfined compressive strength of silty soil stabi-lized with bottom ash, jute and steel fibers via artificial intelligence”, Geomech Eng, 12(3), 441-464.
[22] Chore, H. S., & Magar, R. B. (2017). “Prediction of unconfined compressive and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes using multiple linear regression and artificial neural network”, Advances in Computational Design, 2, 225–240.
[23] Sihag, P., Suthar, M., & Mohanty, S. (2019). “Estimation of UCS-FT of Dispersive Soil Stabilized with Fly Ash, Cement Clinker and GGBS by Artificial Intelligence”, Iranian Journal of Science and Technology, Trans-actions of Civil Engineering, 1-12.
[24] Ghanizadeh, A., Bayat, M., Tavana Amlashi, A., & Rahrovan, M. (2019). “Prediction of unconfined com-pressive strength of clay subgrade soil stabilized with Portland cement and lime using Group Method of Data Handling (GMDH)”, Journal of Transportation Infrastructure Engineering, 5(1), 77-96.
[25] Mohanty, S., Roy, N., Singh, S. P., & Sihag, P. (2019). “Estimating the Strength of Stabilized Dispersive Soil with Cement Clinker and Fly Ash”, Geotechnical and Geological Engineering, 37(4), 2915-2926.
[26] ASTM International. (2015). ASTM D 1557: standard Test Methods for Laboratory Compaction Charac-teristics of Soil Using Modified Effort (56,000 ft-lbf/ft3 (2,700 kN-m/m3)). In Annual book of ASTM stand-ards 2015 (pp. 19428-2959). West Conshohocken, PA: American Society for Testing & Materials.
[27] ASTM International. (2015). ASTM D 3282: Standard Test Method Practice for Classification of Soils & Soil-Aggregate Mixtures for Highway Construction Purposes. In Annual book of ASTM standards 2015 (pp. 19428-2959). West Conshohocken, PA: American Society for Testing & Materials.
[28] ASTM International. (2015). ASTM D 2487: Standard Test Method Practice for Classification of Soils for Engineering Purposes (Unified Soil Classification System). In Annual book of ASTM standards 2015 (pp. 19428-2959). West Conshohocken, PA: American Society for Testing & Materials.
[29] ASTM International. (2014). ASTM D 854: standard test methods for Specific Gravity of Soil Solids by Water Pycnometer on Soil. In Annual book of ASTM standards 2014 (pp. 19428-2959). West Conshohocken, PA: American Society for Testing & Materials.
[30] ASTM International. (2014). ASTM D 4318: Standard Test Methods for Liquid Limit, Plastic Limit, & Plasticity Index of Soils. In Annual book of ASTM standards 2014 (pp. 19428-2959). West Conshohocken, PA: American Society for Testing & Materials.
[31] ASTM International. (2008). ASTM D 427: standard Test Method for Shrinkage Factors of Soils by The Mercury Method. In Annual book of ASTM standards 2008 (pp. 19428-2959). West Conshohocken, PA: American Society for Testing & Materials.
[32] ASTM International. (2013). ASTM D 4972: Standard Test Method Method for PH of Soils. In Annual book of ASTM standards 2013 (pp. 19428-2959). West Conshohocken, PA: American Society for Testing & Materials.
[33] ASTM International. (2015). ASTM D 2166: standard Test Method for Unconfined Compressive Strength of Cohesive Soil. In Annual book of ASTM standards 2015 (pp. 19428-2959). West Conshohocken, PA: Amer-ican Society for Testing & Materials.
[34] ASTM International. (2014). ASTM D 496: Standard Test Method Method for Splitting Tensile Strength of Cylindrical Concrete Specimens. In Annual book of ASTM standards 2014 (pp. 19428-2959). West Con-shohocken, PA: American Society for Testing & Materials.
[35] Haykin, S. (2001). Neural networks: principles and practice. Bookman, 11, 900.
[36] Freeman, J. A., & Skapura, D. M. (1992). “Backpropagation”, Neural Networks Algorithm Applications and Programming Techniques, 40, 89-125.
[37] Werbos, P. (1974). Beyond regression:" new tools for prediction and analysis in the behavioral sciences. Ph. D. dissertation, Harvard University.
[38] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). “Learning representations by back-propagating errors”, Nature, 323(6088), 533-536.
[39] Jang, J. R. (1993). “ANFIS: adaptive-network-based fuzzy inference system”, IEEE Transactions on Sys-tems, Man, and Cybernetics, 23(3), 665-685.
[40] Kisi, O., Haktanir, T., Ardiclioglu, M., Ozturk, O., Yalcin, E., & Uludag, S. (2009). “Adaptive neuro-fuzzy computing technique for suspended sediment estimation”, Advances in Engineering Software, 40(6), 438-444.
CAPTCHA Image