پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 676 |
| تعداد مقالات | 9,865 |
| تعداد مشاهده مقاله | 69,972,729 |
| تعداد دریافت فایل اصل مقاله | 49,266,084 |
An Innovative Approach to Estimate Chloride Diffusion Coefficient in Submerged Concrete Structures Using Soft Computing | ||
| Journal of Rehabilitation in Civil Engineering | ||
| مقاله 5، دوره 11، شماره 3 - شماره پیاپی 31، آبان 2023، صفحه 88-106 اصل مقاله (1.69 M) | ||
| نوع مقاله: Regular Paper | ||
| شناسه دیجیتال (DOI): 10.22075/jrce.2022.28128.1697 | ||
| نویسندگان | ||
| Seyyed Ali Habibi1؛ Ali Hemmati* 2؛ Hosein Naderpour3 | ||
| 1Ph.D. Student, Department of Civil Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran | ||
| 2Assistant Professor, Department of Civil Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran | ||
| 3Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran | ||
| تاریخ دریافت: 30 مرداد 1401، تاریخ بازنگری: 30 مهر 1401، تاریخ پذیرش: 15 آذر 1401 | ||
| چکیده | ||
| Corrosion is one of the most important and common factors in the destruction of structures. Among all kinds of structures, corrosion of submerged structures is of great importance and prevalence due to the impossibility of direct visibility, high reconstruction cost and special environmental conditions. The work done in the field of corrosion of these structures has mainly dealt with modeling the problem in the form of mathematical formulation or using soft computing methods. The work that has established the connection between these two methods has not been done, to the best of our knowledge. This article aims to develop a model in order to estimate the chloride diffusion coefficient in rebar corrosion in submerged concrete structures. Present study seeks to address the estimation of chloride diffusion coefficient, which is one of the determinant factors in computing the corrosion time/rate of rebar’s. In this article, using the Monte Carlo sampling method and the formulas available for chloride diffusion coefficient, we produced 2000 artificial data samples. A variety of methods such as support vector machines (e.g., linear, quadratic, cubic, Gaussian), K-nearest neighbors (fine, medium, coarse KNN), and two methods of ensemble learning (bagged tree, subspace discriminant) are applied to predict the chloride diffusion coefficient. The results indicated that the quadratic support vector method (with 93.5% accuracy) is the best technique in estimating the chloride diffusion coefficient. Best KNN model (medium KNN) and best ensemble method (bagged tree) have accuracy of 59.9% and 81.3%, resp. | ||
| کلیدواژهها | ||
| Corrosion؛ Markov chain؛ Support vector machine؛ Ensemble learning؛ K-Nearest neighbors | ||
| مراجع | ||
|
[1] Karuppanasamy J, Pillai RG. Statistical Distributions for the Corrosion Rates of Conventional and Prestressing Steel Reinforcement Embedded in Chloride Contaminated Mortar. CORROSION 2017;73:1119–31. https://doi.org/10.5006/2330.
[2] Ramezanianpou AA, Jahangiri E, Moodi F, Ahmadi B. Assessment of the Service Life Design Model Proposed by fib for the Persian Gulf Region. J Oceanogr 2014;5:101–12.
[3] Chou JS, Ngo NT, Chong WK. The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate. Eng Appl Artif Intell 2017;65:471–83. https://doi.org/10.1016/j.engappai.2016.09.008.
[4] Sadowski L. Non-destructive investigation of corrosion current density in steel reinforced concrete by artificial neural networks. Arch Civ Mech Eng 2013;13:104–11. https://doi.org/10.1016/j.acme.2012.10.007.
[5] Jahangir H, Nikkhah Z, Rezazadeh Eidgahee D, Esfahani MR. Performance Based Review and Fine-Tuning of TRM-Concrete Bond Strength Existing Models. J Soft Comput Civ Eng 2023;7:43–55. https://doi.org/10.22115/scce.2022.349483.1476.
[6] Jiménez-Come MJ, Muñoz E, García R, Matres V, Martín ML, Trujillo F, et al. Pitting corrosion behaviour of austenitic stainless steel using artificial intelligence techniques. J Appl Log 2012;10:291–7. https://doi.org/10.1016/j.jal.2012.07.005.
[7] Papadimitropoulos VC, Tsikas PK, Chassiakos AP. Modeling the Influence of Environmental Factors on Concrete Evaporation Rate. J Soft Comput Civ Eng 2020;4:79–97. https://doi.org/10.22115/scce.2020.246071.1254.
[8] Mousavifard SM, Attar MM, Ghanbari A, Dadgar M. Application of artificial neural network and adaptive neuro-fuzzy inference system to investigate corrosion rate of zirconium-based nano-ceramic layer on galvanized steel in 3.5% NaCl solution. J Alloys Compd 2015;639:315–24. https://doi.org/10.1016/j.jallcom.2015.03.052.
[9] Chen B, Zang C. Artificial immune pattern recognition for structure damage classification. Comput Struct 2009;87:1394–407. https://doi.org/10.1016/j.compstruc.2009.08.012.
[10] Taffese WZ, Sistonen E. Significance of chloride penetration controlling parameters in concrete: Ensemble methods. Constr Build Mater 2017;139:9–23. https://doi.org/10.1016/j.conbuildmat.2017.02.014.
[11] Fakharian P, Rezazadeh Eidgahee D, Akbari M, Jahangir H, Ali Taeb A. Compressive strength prediction of hollow concrete masonry blocks using artificial intelligence algorithms. Structures 2023;47:1790–802. https://doi.org/10.1016/j.istruc.2022.12.007.
[12] Kontoni D-PN, Onyelowe KC, Ebid AM, Jahangir H, Rezazadeh Eidgahee D, Soleymani A, et al. Gene Expression Programming (GEP) Modelling of Sustainable Building Materials including Mineral Admixtures for Novel Solutions. Mining 2022;2:629–53. https://doi.org/10.3390/mining2040034.
[13] Ghanizadeh AR, Ghanizadeh A, Asteris PG, Fakharian P, Armaghani DJ. Developing bearing capacity model for geogrid-reinforced stone columns improved soft clay utilizing MARS-EBS hybrid method. Transp Geotech 2023;38:100906. https://doi.org/10.1016/j.trgeo.2022.100906.
[14] Khademi A, Behfarnia K, Kalman Šipoš T, Miličević I. The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete. Comput Eng Phys Model 2021;4:1–25. https://doi.org/10.22115/cepm.2021.297016.1181.
[15] Rezazadeh Eidgahee D, Haddad A, Naderpour H. Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling. Sci Iran 2019;26:3233–44. https://doi.org/10.24200/sci.2018.5663.1408.
[16] Naderpour H, Haji M, Mirrashid M. Shear capacity estimation of FRP-reinforced concrete beams using computational intelligence. Structures 2020;28:321–8. https://doi.org/10.1016/j.istruc.2020.08.076.
[17] Naderpour H, Mirrashid M. Proposed soft computing models for moment capacity prediction of reinforced concrete columns. Soft Comput 2020;24:11715–29. https://doi.org/10.1007/s00500-019-04634-8.
[18] Naderpour H, Mirrashid M. Moment capacity estimation of spirally reinforced concrete columns using ANFIS. Complex Intell Syst 2020;6:97–107. https://doi.org/10.1007/s40747-019-00118-2.
[19] Atha DJ, Jahanshahi MR. Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection. Struct Heal Monit 2018;17:1110–28. https://doi.org/10.1177/1475921717737051.
[20] Naderpour H, Sharei M, Fakharian P, Heravi MA. Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-NN and GEP. J Soft Comput Civ Eng 2022;6:66–87. https://doi.org/10.22115/SCCE.2022.283486.1308.
[21] Rezazadeh Eidgahee D, Jahangir H, Solatifar N, Fakharian P, Rezaeemanesh M. Data-driven estimation models of asphalt mixtures dynamic modulus using ANN, GP and combinatorial GMDH approaches. Neural Comput Appl 2022;34:17289–314. https://doi.org/10.1007/s00521-022-07382-3.
[22] Onyelowe KC, Rezazadeh Eidgahee D, Jahangir H, Aneke FI, Nwobia LI. Forecasting Shear Parameters, and Sensitivity and Error Analyses of Treated Subgrade Soil. Transp Infrastruct Geotechnol 2022. https://doi.org/10.1007/s40515-022-00225-7.
[23] Salahudeen AB, Jalili M, Eidgahee DR, Onyelowe KC, Kabiri MK. Prediction of Durability, Resilient Modulus and Resistance Value of Cement Kiln Dust-Stabilized Expansive Clay for Flexible Pavement Application Using Artificial Neural Networks. vol. 164. Cham: Springer International Publishing; 2022. https://doi.org/10.1007/978-3-030-77230-7.
[24] Naderpour H, Rezazadeh Eidgahee D, Fakharian P, Rafiean AH, Kalantari SM. A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling. Eng Sci Technol an Int J 2020;23:382–91. https://doi.org/10.1016/j.jestch.2019.05.013.
[25] Naderpour H, Noormohammadi E, Fakharian P. Prediction of Punching Shear Capacity of RC Slabs using Support Vector Machine. Concr Res 2017;10:95–107. https://doi.org/10.22124/jcr.2017.2417.
[26] Angst UM. Predicting the time to corrosion initiation in reinforced concrete structures exposed to chlorides. Cem Concr Res 2019;115:559–67. https://doi.org/10.1016/j.cemconres.2018.08.007.
[27] Khan MU, Ahmad S, Al-Gahtani HJ. Chloride-Induced Corrosion of Steel in Concrete: An Overview on Chloride Diffusion and Prediction of Corrosion Initiation Time. Int J Corros 2017;2017:1–9. https://doi.org/10.1155/2017/5819202.
[28] Femenias Y, Angst U, Moro F, Elsener B. Development of a Novel Methodology to Assess the Corrosion Threshold in Concrete Based on Simultaneous Monitoring of pH and Free Chloride Concentration. Sensors 2018;18:3101. https://doi.org/10.3390/s18093101.
[29] Ghanooni Bagha M, Asgarani S. Influence of effective chloride corrosion parameters variations on corrosion initiation. Modares Civ Eng J 2017;17.
[30] Nogueira CG, Leonel ED. Probabilistic models applied to safety assessment of reinforced concrete structures subjected to chloride ingress. Eng Fail Anal 2013;31:76–89. https://doi.org/10.1016/j.engfailanal.2013.01.023. | ||
|
آمار تعداد مشاهده مقاله: 613 تعداد دریافت فایل اصل مقاله: 353 |
||