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پیش‌بینی مقاومت فشاری و نفوذ یون کلرید نمونههای بتنی با تخمین بیزین و انجام تحلیل‌های مختلف قابلیت اعتماد | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 23، دوره 53، شماره 8، آبان 1400، صفحه 3555-3576 اصل مقاله (2.29 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2020.18029.6743 | ||
| نویسندگان | ||
| مهدی کماسی* 1؛ سیدعلی حسن زاده2 | ||
| 1استادیار گروه عمران دانشگاه آیت الله العظمی بروجردی (ره) | ||
| 2دانش آموخته کارشناسی ارشد مهندسی سازه، دانشگاه رازی | ||
| چکیده | ||
| مقاومت فشاری و نفوذ یون کلرید (RCPT) به ترتیب از مشخصه‌‌های مهم مکانیکی و دوام بتن می‌باشند. استفاده از روش‌های تحلیلی مانند فرمول‌ها، نمودارها و نموگرام‌ها برای پیش‌بینی و تحلیل قابلیت اعتماد مقاومت فشاری بتن و RCPT همواره با ضعف‌های زیادی همراه بوده است و همچنین روش‌های محاسبات نرم نیز با وجود دارا بودن دقت مناسب در پیش‌بینی مقاومت فشاری و RCPT، به قابلیت اعتماد هر یک از این پارامترها نپرداخته‌اند. به همین خاطر در این پژوهش از روش احتمالاتی و خطی بیزین استفاده شده است. برای این منظور، براساس تعدادی نمونه بتنی، برای هر یک از پارامترهای مقاومت فشاری و RCPT یک رابطه احتمالاتی براساس ورودی‌های مسأله و خروجی‌های خواسته شده، پیشنهاد شده است. دقت هر یک از روابط پیشنهادی مورد بررسی قرار گرفت و پس از صحت روابط داده شده، از آن‌ها برای تحلیل قابلیت اعتماد استفاده شد. در بخش بعدی این پژوهش، برای بررسی تحلیل قابلیت اعتماد از روش‌های تحلیل قابلیت اعتماد مرتبه اول (FORM)، نمونهگیری مونتکارلو و نمونهگیری پیشین برای بررسی قابلیت اعتماد مقاومت فشاری و RCPT استفاده شده است. تحلیل‌های حاصل از هر سه روش در ارزیابی مقاومت فشاری و RCPT نشان دادند که نتایج هر سه روش به هم بسیار نزدیک می‌باشند. بنابراین استفاده از روش FORM که یک روش خطی و با هزینه محاسباتی پایین‌تر است، می‌تواند گزینه مناسبی برای تحلیل قابلیت اعتماد مقاومت فشاری و RCPT بتن باشد. | ||
| کلیدواژه‌ها | ||
| نمونههای بتن؛ تخمین بیزین؛ تحلیل مرتبه اول قابلیت اعتماد؛ نمونهگیری مونتکارلو؛ نمونهگیری پیشین | ||
| موضوعات | ||
| تست های آزمایشگاهی؛ تکنولوژی بتن؛ شبکه های عصبی؛ قابلیت اطمینان؛ مکانیک شکست | ||
| عنوان مقاله [English] | ||
| CS and RCPT Prediction of Concrete Samples Using Bayesian Inference and Performing Different Reliability Analyzes | ||
| نویسندگان [English] | ||
| Mehdi Komasi1؛ Seyed Ali Hassanzadeh2 | ||
| 1Assistant Professor, Department of Civil Engineering, University of Ayatollah ozma Borujerdi | ||
| 2M.Sc. of Structural Engineering, Razi University | ||
| چکیده [English] | ||
| Compression strength (CS) and rapid chloride permeability test (RCPT) are very significant parameters in mechanical and durability properties in concrete, respectively. Analytical methods such as formulas and graphs for prediction and reliability of CS and RCPT in concrete samples are gathered with many problems. Many soft computing methods are very accurate in the prediction of CS and RCPT but these methods are deterministic or have not reliability tools. For these reasons, Bayesian inference is used which is a probabilistic and linear method. For this purpose, according to some of the concrete samples, a probabilistic relation is proposed for each CS and RCPT. The accuracy of each proposed formula is tested, and after verification of them, reliability analysis is performed. In this study, the first-order reliability method (FORM), Monte-Carlo sampling (MCS), and histogram sampling are used for reliability analysis. Each of these methods has unique properties that FORM is linear and has a very short time-consuming. MCS and histogram sampling are nonlinear and have high time-consuming but their accuracy are very high. Histogram sampling is similar to MCS but in this type of analysis, reliability results for any outcomes are given, and time-consuming in this method is very high. A three-method analysis of CS and RCPT showed that the results are closed together. So, using FORM because of use easily and save time-consuming can be a reasonable choice for reliability analysis of CS and RCPT in concrete samples. | ||
| کلیدواژه‌ها [English] | ||
| Concrete samples, Bayesian inference, FORM, Monte-Carlo sampling, Histogram sampling | ||
| مراجع | ||
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[1] P. Gardoni, K.M. Nemati, T. Noguchi, Bayesian statistical framework to construct probabilistic models for the elastic modulus of concrete, Journal of materials in civil engineering, 19(10) (2007) 898-905. [2] S. Chithra, S.S. Kumar, K. Chinnaraju, F.A. Ashmita, A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks, Construction and Building Materials, 114 (2016) 528-535. [3] A. Sadrmomtazi, J. Sobhani, M.A. Mirgozar, Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS, Construction and Building Materials, 42 (2013) 205-216. [4] M. Hosseini, S. Hosseini, Introducing new equation for predicting penetration rate of tunnel boring machine, Amirkabir Journal of Civil Engineering, 49(2) (2017) 313-322. [5] S. Jafari, S.S. Mahini, Lightweight concrete design using gene expression programing, Construction and Building Materials, 139 (2017) 93-100. [6] H. Naderpour, A.H. Rafiean, P. Fakharian, Compressive strength prediction of environmentally friendly concrete using artificial neural networks, Journal of Building Engineering, 16 (2018) 213-219. [7] S.V. Tosee, M. Nikoo, Neuro-fuzzy systems in determining light weight concrete strength, Journal of Central South University, 26(10) (2019) 2906-2914. [8] J. Sobhani, M. Najimi, Numerical study on the feasibility of dynamic evolving neural-fuzzy inference system for approximation of compressive strength of dry-cast concrete, Applied Soft Computing, 24 (2014) 572-584. [9] Y. Mori, B.R. Ellingwood, Maintaining reliability of concrete structures. I: Role of inspection/repair, Journal of Structural Engineering, 120(3) (1994) 824-845. [10] M.P. Enright, D.M. Frangopol, Condition prediction of deteriorating concrete bridges using Bayesian updating, Journal of Structural Engineering, 125(10) (1999) 1118-1125. [11] H. Sohn, K.H. Law, Bayesian probabilistic damage detection of a reinforced‐concrete bridge column, Earthquake engineering & structural dynamics, 29(8) (2000) 1131-1152. [12] D.E. Choe, P. Gardoni, Rosowsky D. Closed-form fragility estimates, parameter sensitivity, and Bayesian updating for RC columns, Journal of Engineering Mechanics, 133(7) (2007) 833-843. [13] R. Giannini, L. Sguerri, F. Paolacci, S. Alessandri, Assessment of concrete strength combining direct and NDT measures vi a Bayesian inference, Engineering structures, 64 (2014) 68-77. [14] B. Han, T.Y. Xiang, H.B. Xie, A Bayesian inference framework for predicting the long-term deflection of concrete structures caused by creep and shrinkage, Engineering Structures, 142 (2017) 46-55. [15] S.A. Faroz, N.N. Pujari, S. Ghosh, Reliability of a corroded RC beam based on Bayesian updating of the corrosion model, Engineering Structures, 126 (2016) 457-468. [16] H. Sousa, L.O. Santos, M. Chryssanthopoulos, Quantifying monitoring requirements for predicting creep deformations through Bayesian updating methods, Structural Safety, 76 (2019) 40-50. [17] A. Fleischhacker, O. Ghonima, T. Schumacher, Bayesian Survival Analysis for US Concrete Highway Bridge Decks, Journal of Infrastructure Systems. 26(1) (2020) 04020001. [18] M. Bagheri, A. Chahkandi, H. Jahangir, Seismic Reliability Analysis of RC Frames Rehabilitated by Glass Fiber-Reinforced Polymers, International Journal of Civil Engineering, 17(11) (2019) 1785-1797. [19] M. Shakouri, D. Trejo, Estimating the critical chloride threshold of reinforcing steel in concrete using a hierarchical Bayesian model, Sustainable and Resilient Infrastructure, 4(4) (2019) 152-72. [20] A. Nahvi, M.K. Sadoughi, A. Arabzadeh, A. Sassani,C. Hu, H. Ceylan, and S. Kim, Multi-objective bayesian optimization of super hydrophobic coatings on asphalt concrete surfaces, Journal of Computational Design and Engineering, 6(4) (2019) 693-704. [21] S.S. Gilan, H.B. Jovein, A.A. Ramezanianpour, Hybrid support vector regression–Particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin, Construction and Building Materials, 34 (2012) 321-329. [22] M. Mahsuli, T. Haukaas, Computer program for multimodel reliability and optimization analysis, Journal of Computing in Civil Engineering, 27(1) (2013) 87-98. [23] M. Naderi, M. Mahsuli, Uncertainty Quantification in Modeling of Steel Structures using Timoshenko Beam, Journal of Structural and Construction Engineering, 6(1) (2019) 27-42. (In Persian). [25] T. Haukaas, Civil 518: Reliability and structure safety, University of British Columbia, Vancouver, BC, 2018. [26] A.H. Gandomi, S. Mohammadzadeh, J.L. Pérez-Ordó˜nezc, A.H. Alavi, Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups, Applied Soft Computing, 19 (2014) 112–120. [27] Y. Sharifi, M. Hosseinpour, Adaptive neuro-fuzzy inference system and stepwise regression for compressive strength assessment of concrete containing metakaolin, International Journal of Optimization in Civil Engineering, 9(2) (2019) 251-272. [28] M.S. Asghshahr, A. Rahai, H. Ashrafi, Prediction of chloride content in concrete using ANN and CART, Magazine of Concrete Research. 68(21) (2016) 1085-1098. [29] H. Koo, A. Der Kiureghian, FORM, SORM and Simulation Techniques for Nonlinear Random Vibrations. Report No. UCB/SEMM-2003/01, Department of Civil & Environmental Engineering, University of California, Berkeley, CA, 2003. [30] E. Nikolaidis, D.M. Ghiocel, S. Singhal, editors, Engineering design reliability handbook, CRC Press, MA, 2004. [31] A.M. Hasofer, N.C. Lind, Exact and invariant second-moment code format, Journal of the Engineering Mechanics Division, ASCE, 100(1) (1974) 111–121. [32] R. Rackwitz, B. Fiessler, Structural reliability under combined load sequences, Computers & Structures, 9 (1978) 489–494. [33] P.L. Liu, A. Der Kiureghian, Optimization algorithms for structural reliability, Structural Safety, 9(3) (1990) 161–177. [34] T. Haukaas, A. Der Kiureghian, Strategies for Finding the Design Point in Nonlinear Finite Element Reliability Analysis, Probabilistic Engineering Mechanics, 21(2) (2006) 133-147. [35] P. Rostami, M. Mahsuli, Risk-Optimal Arrangement of Stiffeners in Steel Plate Shear Walls with Door Opening, Frontiers in Built Environment, 4 (2018) 59. [36] H. Naderpour, A. Kheyroddin, S. Mortazavi, Risk Assessment in Bridge Construction Projects in Iran Using Monte Carlo Simulation Technique, Practice Periodical on Structural Design and Construction, 24(4) (2019) 04019026. [37] S.A. Pari, G. Habibagahi, A. Ghahramani, K. Fakharian, Reliability-Based Calibration of Resistance Factors in LRFD Method for Driven Pile Foundations on Inshore Regions of Iran, International Journal of Civil Engineering, 17(12) (2019) 1859-70. [38] S. Safaei, H. Naderpour, M. Gerami, Reliability assessment of RC frames rehabilitated by eccentrically braces having vertical shear link, SN Applied Sciences, 2(3) (2020)1-4. [39] M. Haji, H. Naderpour, A. Kheyroddin, Experimental study on influence of proposed FRP-strengthening techniques on RC circular short columns considering different types of damage index, Composite Structures, 209 (2019) 112–128. [40] M. Hussein, T. Sayed, K. Ismail, A. Van Espen, Calibrating road design guides using risk-based reliability analysis, Journal of Transportation Engineering, 140(9) (2014) 04014041. | ||
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