ارزیابی مؤلفه‌های ساخت‌وساز با بتن بازیافتی سازگار با محیط‌زیست با رویکرد مدل‌سازی شبکه عصبی مصنوعی و سیستم استنتاج فازی - عصبی تطبیقی بهینه شده با الگوریتم فراابتکاری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترا عمران، دانشگاه آزاد رودهن

2 گروه عمران، دانشگاه آزاد رودهن، رودهن، ایران

3 استادیارگروه عمران، دانشگاه آزاد رودهن، رودهن، ایران

10.22124/jcr.2025.29506.1689

چکیده

در این مطالعه، مدل‌سازی با مؤلفه‌های ساخت‌وساز با بتن بازیافتی سازگار با محیط‌زیست مورد بررسی قرار گرفته است. برای مدل‌سازی مقاومت فشاری بتن بازیافتی از دو روش شبکه عصبی مصنوعی و سیستم استنتاج فازی - عصبی تطبیقی استفاده شد. سپس برای افزایش عملکرد روش‌های هوشمند و جلوگیری از رویکرد سعی و خطا برای دستیابی به بهینه‌ترین مقادیر پارامترهای هر مدل، از الگوریتم بهینه‌سازی ازدحام ذرات استفاده شد. داده‌های طرح اختلاط از پیشینه تحقیق برای ایجاد مدل‌های پیشنهادی، 201 داده آزمایشگاهی بود که در جهت برآورد مقاومت فشاری بتن بازیافتی جمع‌آوری شـد. سپس برای تعیین بهترین پارامترهای ورودی به مدل، چهار سناریو تعریف گردید که از میان آنها، سناریو برتر انتخاب شد. همچنین برای این شاخص روش ANFIS-FCM نسبت به باقی روش‌های بر پایه نروفازی، از دقت بالاتری برخوردار بوده است. در مرحله تست نیز، روش شبکه عصبی مصنوعی بر پایه الگوریتم ازدحام ذرات، از عملکرد بهتری نسبت به روش ANN برخوردار بود. اما روش ANFIS-FCM-PSO بر خلاف اینکه در مرحله آموزش کمی از روش ANFIS-FCM ضعیف‌تر عمل نموده است، اما در مرحله تست بر اساس سه شاخص ارائه شده عملکردی بهتر از باقی روش‌های پیشنهادی داشته است. نتایج حاصل از تحلیل عدم قطعیت نشانگر آن بود که روش پیشنهادی بافاصله بسیار کمی در ترم خطای میانگین پیش‌بینی داشته‌اند. همچنین در میان روش‌های هوشمند پیشنهادی، روش ANFIS-FCM-PSO با اختلاف اندکی نسبت به دیگر مدل‌ها دارای عملکرد بهتری است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation of construction components with eco-friendly recycled concrete with artificial neural network modeling approach and adaptive neural fuzzy inference system optimized with meta-heuristic algorithm

نویسندگان [English]

  • Mahyar azizkhani 1
  • Ali Asghar AmirKardoost 2
  • davood sedaghat shayegan 3
1 PhD candidate, Islamic azad university, roudehen branch
2 Department of civil engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
3 Department of civil engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
چکیده [English]

]n this study, modeling with environmentally friendly recycled concrete construction components has been investigated. To model the compressive strength of recycled concrete, two methods of artificial neural network and adaptive neural fuzzy inference system were used. Then, in order to increase the performance of smart methods and prevent the trial and error approach to achieve the most optimal parameter values of each model, the particle swarm optimization algorithm was used. The data of the mixing plan from the research background to create the proposed models were 201 laboratory data that were collected to estimate the compressive strength of recycled concrete. Then, to determine the best input parameters to the model, four scenarios were defined, from which the best scenario was selected. Also, for this index, the ANFIS-FCM method is more accurate than the rest of the neurophasic based methods. In the test phase, the artificial neural network method based on the particle swarm algorithm had a better performance than the ANN method. However, the ANFIS-FCM-PSO method performed slightly weaker than the ANFIS-FCM method in the training phase, but it performed better than the rest of the proposed methods in the test phase based on the three presented indicators. The results of the uncertainty analysis indicated that the proposed method had a very small margin of error in the term of average prediction. Also, among the proposed intelligent methods, the ANFIS-FCM-PSO method has better performance than other models with a slight difference.

کلیدواژه‌ها [English]

  • sustainable development
  • recycled concrete
  • compressive strength prediction
  • artificial neural network
  • adaptive neural fuzzy inference system
  • particle swarm optimization algorithm

مراجع

[1] Zhang, P., Sun, X., Wang, F., & Wang, J. (2023). Mechanical properties and durability of geopolymer recycled aggregate concrete: A review. Polymers, 15(3), 615.
[2] Wang, D., Lu, C., Zhu, Z., Zhang, Z., Liu, S., Ji, Y., & Xing, Z. (2023). Mechanical performance of recycled aggregate concrete in green civil engineering. Case Studies in Construction Materials, 19, e02384.
[3] Tejas, S., & Pasla, D. (2023). Assessment of mechanical and durability properties of composite cement-based recycled aggregate concrete. Construction and Building Materials, 387, 131620.
[4] Xu, W., Tang, Z., Song, Y., Xie, Y., Lei, B., Yu, H., & Kai, M. (2023). Drying shrinkage of geopolymeric recycled aggregate concrete. Construction and Building Materials, 395, 132220.
[5] Wang, S., Xia, P., Gong, F., Zeng, Q., Chen, K., & Zhao, Y. (2024). Multi objective optimization of recycled aggregate concrete based on explainable machine learning. Journal of Cleaner Production, 445, 141045.
[6] Abd, Abbas M., and Suhad M. Abd. "Modelling the strength of lightweight foamed concrete using support vector machine (SVM)." Case Studies in Construction Materials 6 (2017): 8-15.
[7] Kiani, Behnam, et al. "New formulation of compressive strength of preformed-foam cellular concrete: an evolutionary approach." Journal of Materials in Civil Engineering 28.10 (2016): 04016092.
[8] Gholampour, Aliakbar, Amir H. Gandomi, and Togay Ozbakkaloglu. "New formulations for mechanical properties of recycled aggregate concrete using gene expression programming." Construction and Building Materials 130 (2017): 122-145.
[9] Ashrafian, A., Amiri, M. J. T., Rezaie-Balf, M., Ozbakkaloglu, T., & Lotfi-Omran, O. (2018). Prediction of compressive strength and ultrasonic pulse velocity of fiber reinforced concrete incorporating nano silica using heuristic regression methods. Construction and Building Materials, 190, 479-494.
[10] Asteris, P. G., Ashrafian, A., & Rezaie-Balf, M. (2019). Prediction of the compressive strength of self-compacting concrete using surrogate models. Computers and Concrete, 24(2), 137-150.
[11] Shahrokhishahraki, M., Malekpour, M., Mirvalad, S., & Faraone, G. (2024). Machine learning predictions for optimal cement content in sustainable concrete constructions. Journal of Building Engineering, 82, 108160.
[12] Zhang, S., Chen, W., Xu, J., & Xie, T. (2024). Use of interpretable machine learning approaches for quantificationally understanding the performance of steel fiber-reinforced recycled aggregate concrete: From the perspective of compressive strength and splitting tensile strength. Engineering Applications of Artificial Intelligence, 137, 109170.
[13] Dinesh, A., & Prasad, B. R. (2024). Predictive models in machine learning for strength and life cycle assessment of concrete structures. Automation in Construction, 162, 105412.
[14] Sedaghat Shayegan D. Optimum cost design of reinforced concrete slabs using a metaheuristic algorithm, Int J Optim Civil Eng 2022; 12(4): 545-55.
[15] Shahebrahimi, SS., Lork, A., Sedaghat Shayegan, D. (2022). KNOWLEDGE MANAGEMENT TO INVESTIGATE THE FAILURE FACTORS IN MANAGING OF GAS AND OIL INDUSTRY TRANSMISSION LINES PROJECTS, Iran University of Science & Technology 12 (2), 215-233
[16] Saberi AA, Ahmadi H, Sedaghat Shayegan D, Amirkardoust A (2023)
Prediction of electricity consumption using three meta-heuristic
algorithms. Int J Optim Civil Eng. 13(1):111–125
[17] Sedaghat Shayegan D, Lork A, Hashemi AH. optimum cost design of reinforced concrete slabs using Mouth Brooding Fish (MBF) algorithm, J Appl Eng Sci 2020; 10(23): ISSUE 1 ART.NO. 290: pp. 95-100.
[18] JACOB, L. L. G. (2011). Effects of Recycled Aggregates on Concrete Properties (Doctoral dissertation).
 [19] Casuccio, M., Torrijos, M. C., Giaccio, G., & Zerbino, R. (2008). Failure mechanism of recycled aggregate concrete. Construction and Building Materials, 22(7), 1500-1506.
[20] Etxeberria, M., Vázquez, E., Marí, A., & Barra, M. (2007). Influence of amount of recycled coarse aggregates and production process on properties of recycled aggregate concrete. Cement and concrete research, 37(5), 735-742.
[21] Rao, M. C., Bhattacharyya, S. K., & Barai, S. V. (2011). Influence of field recycled coarse aggregate on properties of concrete. Materials and Structures, 44(1), 205-220.
[22] Yang, K. H., Chung, H. S., & Ashour, A. F. (2008). Influence of Type and Replacement Level of Recycled Aggregates on Concrete Properties.
[23] Kou, S. C., & Poon, C. S. (2008). Mechanical properties of 5-year-old concrete prepared with recycled aggregates obtained from three different sources. Magazine of Concrete Research, 60(1), 57-64
[24] Zheng, C., Lou, C., Du, G., Li, X., Liu, Z., & Li, L. (2018). Mechanical properties of recycled concrete with demolished waste concrete aggregate and clay brick aggregate. Results in Physics, 9, 1317-1322
[25] Vieira, J. P. B., Correia, J. R., & De Brito, J. (2011). Post-fire residual mechanical properties of concrete made with recycled concrete coarse aggregates. Cement and Concrete Research, 41(5), 533-541.
[26] Alengaram, U. J., Salam, A., Jumaat, M. Z., Jaafar, F. F., & Saad, H. B. (2011). Properties of high-workability concrete with recycled concrete aggregate. Materials Research, 14(2), 248-255
[27] Duan, Z. H., & Poon, C. S. (2014). Properties of recycled aggregate concrete made with recycled aggregates with different amounts of old adhered mortars. Materials & Design, 58, 19-29
[28] Folino, P., & Xargay, H. (2014). Recycled aggregate concrete–mechanical behavior under uniaxial and triaxial compression. Construction and Building Materials, 56, 21-31.
[29] Kotrayothar, D. (2012). Recycled aggregate concrete for structural applications (Doctoral dissertation, University of Western Sydney).
[30] Thomas, J., Thaickavil, N. N., & Wilson, P. M. (2018). Strength and durability of concrete containing recycled concrete aggregates. Journal of Building Engineering, 19, 349-365
[31] Zega, C. J., & Di Maio, A. A. (2009). Recycled concrete made with different natural coarse aggregates exposed to high temperature. Construction and building materials, 23(5), 2047-2052.
[32] Tung Y, Yen B, Hydrosystem engineering uncertainty analysis, McGraw Hill Book Company, NY, USA, 2006.

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