AI-Based Formulation for Mechanical and Workability Properties of Eco-Friendly Concrete Made by Waste Foundry Sand
Publication: Journal of Materials in Civil Engineering
Volume 33, Issue 4
Abstract
The casting process creates a significant amount of waste foundry sand (WFS). Using WFS as a concrete ingredient reduces the problems associated with the dumping process of these types of wastes, removes/reduces carbon dioxide, and is also considered economical in terms of overall concrete production cost. Besides, WFS has been reported to be a critical parameter affecting the mechanical properties and workability of concrete. Hence, predicting the behavior of concrete using the development of models based on artificial intelligence (AI) algorithms derived from the laboratory data can remarkably improve the project’s efficiency in terms of cost and time. This paper assessed the performance of artificial neural networks (ANNs) to predict the strength parameters of concrete containing WFS (CCWFS). In this regard, a comprehensive laboratory database consisting of 102, 397, 146, 346, and 169 data for the slump, compressive strength, elasticity modulus, splitting tensile strength, and flexural strength of CCWFS were collected from literature, respectively. Seven different variables including waste foundry sand to cement ratio (WFS/C), water to cement ratio (W/C), fine aggregate to the total aggregate ratio (FA/TA), coarse aggregate to cement (CA/C), waste foundry sand to the fine aggregate ratio (WFS/FA), 1,000 superplasticizer to cement ratio (1,000SP/C), and age (except for slump), were selected as input. The accuracy of the models was verified by examining 11 different performance indicators. Comparing the predicted and observed data’s overlap percentage indicated that the ANN models are less error-prone than those obtained using multiple linear regression (MLR). Also, the models’ validation and uncertainty analyses showed that all models were within the permissible range. Given the performance evaluation results, it can be concluded that ANNs can be used as a reliable and accurate method for predicting the mechanical properties of CCWFS.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
References
Al-Musawi, A. A., A. A. Alwanas, S. Q. Salih, Z. H. Ali, M. T. Tran, and Z. M. Yaseen. 2020. “Shear strength of SFRCB without stirrups simulation: Implementation of hybrid artificial intelligence model.” Eng. Comput. 36 (1): 1–11. https://doi.org/10.1007/s00366-018-0681-8.
Amlashi, A. T., S. M. Abdollahi, S. Goodarzi, and A. R. Ghanizadeh. 2019a. “Soft computing based formulations for slump, compressive strength, and elastic modulus of bentonite plastic concrete.” J. Cleaner Prod. 230 (Sep): 1197–1216. https://doi.org/10.1016/j.jclepro.2019.05.168.
Amlashi, A. T., P. Alidoust, A. R. Ghanizadeh, S. Khabiri, M. Pazhouhi, and M. S. Monabati. 2020. “Application of computational intelligence and statistical approaches for auto-estimating the compressive strength of plastic concrete.” Eur. J. Environ. Civ. Eng. 1–32. https://doi.org/10.1080/19648189.2020.1803144.
Amlashi, A. T., A. R. Ghanizadeh, H. Abbaslou, and P. Alidoust. 2019b. “Developing three hybrid machine learning algorithms for predicting the mechanical properties of plastic concrete samples with different geometries.” AUT J. Civ. Eng. https://doi.org/10.22060/AJCE.2019.15026.5517.
Ashrafian, A., A. H. Gandomi, M. Rezaie-Balf, and M. Emadi. 2020a. “An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement.” Measurement 152 (4): 107309. https://doi.org/10.1016/j.measurement.2019.107309.
Ashrafian, A., F. Shokri, M. J. T. Amiri, Z. M. Yaseen, and M. Rezaie-Balf. 2020b. “Compressive strength of foamed cellular lightweight concrete simulation: New development of hybrid artificial intelligence model.” Constr. Build. Mater. 230 (Jan): 117048. https://doi.org/10.1016/j.conbuildmat.2019.117048.
Asteris, P. G., A. Ashrafian, and M. Rezaie-Balf. 2019. “Prediction of the compressive strength of self-compacting concrete using surrogate models.” Comput. Concr. 24 (Aug): 137–150.
Asteris, P. G., P. C. Roussis, and M. G. Douvika. 2017. “Feed-forward neural network prediction of the mechanical properties of sandcrete materials.” Sensors 17 (6): 1344. https://doi.org/10.3390/s17061344.
Bakis, R., H. Koyuncu, and A. Demirbas. 2006. “An investigation of waste foundry sand in asphalt concrete mixtures.” Waste Manage. Res. 24 (3): 269–274. https://doi.org/10.1177/0734242X06064822.
Basar, H. M., and N. D. Aksoy. 2012. “The effect of waste foundry sand (WFS) as partial replacement of sand on the mechanical, leaching and micro-structural characteristics of ready-mixed concrete.” Constr. Build. Mater. 35 (Oct): 508–515. https://doi.org/10.1016/j.conbuildmat.2012.04.078.
Behnood, A., and E. M. Golafshani. 2020. “Machine learning study of the mechanical properties of concretes containing waste foundry sand.” Constr. Build. Mater. 243 (May): 118152. https://doi.org/10.1016/j.conbuildmat.2020.118152.
Bui, D.-K., T. Nguyen, J.-S. Chou, H. Nguyen-Xuan, and T. D. Ngo. 2018. “A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete.” Constr. Build. Mater. 180 (Aug): 320–333. https://doi.org/10.1016/j.conbuildmat.2018.05.201.
Chopra, P., R. K. Sharma, and M. Kumar. 2016. “Prediction of compressive strength of concrete using artificial neural network and genetic programming.” In Advances in materials science and engineering 2016. New York: Nova Sciences.
Dash, M. K., S. K. Patro, and A. K. Rath. 2016. “Sustainable use of industrial-waste as partial replacement of fine aggregate for preparation of concrete—A review.” Int. J. Sustainable Built Environ. 5 (2): 484–516. https://doi.org/10.1016/j.ijsbe.2016.04.006.
de Barros Martins, M. A., R. M. Barros, G. Silva, and I. F. S. dos Santos. 2019. “Study on waste foundry exhaust sand, WFES, as a partial substitute of fine aggregates in conventional concrete.” Sustainable Cities Soc. 45 (Feb): 187–196. https://doi.org/10.1016/j.scs.2018.11.017.
Ebrahimpour, A., R. N. Z. R. A. Rahman, D. H. E. Ch’ng, and M. B. A. B. Salleh. 2008. “A modeling study by response surface methodology and artificial neural network on culture parameters optimization for thermostable lipase production from a newly isolated thermophilic Geobacillus sp. strain ARM.” BMC Biotechonol. 8 (1): 96. https://doi.org/10.1186/1472-6750-8-96.
European Commission. 2006. Regulation (EC) No 1013/2006 of the European Parliament and of the Council of 5 April 2006 on shipments of waste. Brussels, Belgium: European Parliament, Council of the European Union.
Fu, Z., J. Mo, L. Chen, and W. Chen. 2010. “Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal.” Mater. Des. 31 (1): 267–277. https://doi.org/10.1016/j.matdes.2009.06.019.
Gandomi, A. H., and D. A. Roke. 2015. “Assessment of artificial neural network and genetic programming as predictive tools.” Adv. Eng. Software 88 (Oct): 63–72. https://doi.org/10.1016/j.advengsoft.2015.05.007.
Ghanizadeh, A. R., H. Abbaslou, A. T. Amlashi, and P. Alidoust. 2018. “Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine.” Front. Struct. Civ. Eng. 13 (1): 215–239. https://doi.org/10.1007/s11709-018-0489-z.
Golafshani, E. M., and A. Behnood. 2018a. “Application of soft computing methods for predicting the elastic modulus of recycled aggregate concrete.” J. Cleaner Prod. 176 (Mar): 1163–1176.
Golafshani, E. M., and A. Behnood. 2018b. “Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete.” Appl. Soft Comput. 64 (Mar): 377–400.
Golafshani, E. M., A. Behnood, and M. Arashpour. 2020. “Predicting the compressive strength of normal and high-performance concretes using ANN and ANFIS hybridized with grey wolf optimizer.” Constr. Build. Mater. 232 (Jan): 117266. https://doi.org/10.1016/j.conbuildmat.2019.117266.
Goldberg, D. E., and K. Deb. 1991. “A comparative analysis of selection schemes used in genetic algorithms.” In Vol. 1 of Foundations of genetic algorithms, 69–93. Amsterdam, Netherlands: Elsevier.
Goldberg, D. E., and J. H. Holland. 1988. “Genetic algorithms and machine learning.” Mach. Learn. 3: 95–99.
Gurumoorthy, N., and K. Arunachalam. 2016. “Micro and mechanical behaviour of treated used foundry sand concrete.” Constr. Build. Mater. 123 (Oct): 184–190. https://doi.org/10.1016/j.conbuildmat.2016.06.143.
Gurumoorthy, N., K. Arunachalam, and J. W. Bang. 2019. “Durability studies on concrete containing treated used foundry sand.” Constr. Build. Mater. 201 (Mar): 651–661. https://doi.org/10.1016/j.conbuildmat.2019.01.014.
Hacene, S. M. A. B., F. Ghomari, F. Schoefs, and A. Khelidj. 2014. “Probabilistic modelling of compressive strength of concrete using response surface methodology and neural networks.” Arabian J. Sci. Eng. 39 (6): 4451–4460. https://doi.org/10.1007/s13369-014-1139-y.
Hagan, M. T., H. B. Demuth, M. H. Beale, and O. De Jesús. 1996. Neural network design. Boston: PWS.
Hammoudi, A., K. Moussaceb, C. Belebchouche, and F. Dahmoune. 2019. “Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates.” Constr. Build. Mater. 209 (Jun): 425–436. https://doi.org/10.1016/j.conbuildmat.2019.03.119.
Haykin, S. S. 2001. Neural networks: A comprehensive foundation. Beijing: Tsinghua University Press.
Hossain, M. U., C. S. Poon, I. M. Lo, and J. C. Cheng. 2016. “Comparative environmental evaluation of aggregate production from recycled waste materials and virgin sources by LCA.” Resour. Conserv. Recycl. 109 (May): 67–77. https://doi.org/10.1016/j.resconrec.2016.02.009.
Iqbal, M. F., Q.-F. Liu, I. Azim, X. Zhu, J. Yang, M. F. Javed, and M. Rauf. 2020. “Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming.” J. Hazard. Mater. 384 (Feb): 121322. https://doi.org/10.1016/j.jhazmat.2019.121322.
Jadhav, S. S., S. N. Tande, and A. C. Dubal. 2017. “Beneficial reuse of waste foundry sand in concrete.” Int. J. Sci. Res. Publ. 7 (3): 74–95.
Javad, S. 1994. Use of waste foundry sand in highway construction, 1–304. West Lafayette, IN: Purdue Univ.
Kashif Ur Rehman, S., Z. Ibrahim, S. A. Memon, M. Aunkor, T. Hossain, M. Faisal Javed, K. Mehmood, and S. M. A. Shah. 2018. “Influence of graphene nanosheets on rheology, microstructure, strength development and self-sensing properties of cement based composites.” Sustainability 10 (3): 822.
Kashif Ur Rehman, S., Z. Ibrahim, S. A. Memon, M. F. Javed, and R. A. Khushnood. 2017. “A sustainable graphene based cement composite.” Sustainability 9 (7): 1229. https://doi.org/10.3390/su9071229.
Kaur, G., R. Siddique, and A. Rajor. 2013. “Micro-structural and metal leachate analysis of concrete made with fungal treated waste foundry sand.” Constr. Build. Mater. 38 (Jan): 94–100. https://doi.org/10.1016/j.conbuildmat.2012.07.112.
Keshtegar, B., M. Bagheri, and Z. M. Yaseen. 2019. “Shear strength of steel fiber-unconfined reinforced concrete beam simulation: Application of novel intelligent model.” Compos. Struct. 212 (Mar): 230–242. https://doi.org/10.1016/j.compstruct.2019.01.004.
Khatib, J., S. Baig, A. Bougara, and C. Booth. 2010. “Foundry sand utilisation in concrete production.” In Proc., 2nd Int. Conf. on Sustainable Construction Materials and Technologies. Coventry, UK: Coventry Univ.
Khatib, J., B. Herki, and S. Kenai. 2013. “Capillarity of concrete incorporating waste foundry sand.” Constr. Build. Mater. 47 (Oct): 867–871. https://doi.org/10.1016/j.conbuildmat.2013.05.013.
Konapure, C. G., and D. J. Ghanate. 2015. “Effect of industrial waste foundry sand as fine aggregate on concrete.” Int. J. Curr. Eng. Technol. 5 (4): 2782–2786.
Kostić, S., and D. Vasović. 2015. “Prediction model for compressive strength of basic concrete mixture using artificial neural networks.” Neural Comput. Appl. 26 (5): 1005–1024. https://doi.org/10.1007/s00521-014-1763-1.
Kumar, A., and D. Rani. 2016. “Performance of concrete using paper sludge ash and foundry sand.” Int. J. Innovative Res. Sci. Eng. Technol. 5 (9): 171–176.
Levenberg, K. 1944. “A method for the solution of certain non-linear problems in least squares.” Q. Appl. Math. 2 (2): 164–168. https://doi.org/10.1090/qam/10666.
Li, M.-F., X.-P. Tang, W. Wu, and H.-B. Liu. 2013. “General models for estimating daily global solar radiation for different solar radiation zones in mainland China.” Energy Convers. Manage. 70 (Jun): 139–148. https://doi.org/10.1016/j.enconman.2013.03.004.
Manoharan, T., D. Laksmanan, K. Mylsamy, P. Sivakumar, and A. Sircar. 2018. “Engineering properties of concrete with partial utilization of used foundry sand.” Waste Manage. (Oxford) 71 (Jan): 454–460. https://doi.org/10.1016/j.wasman.2017.10.022.
Marinković, S., V. Radonjanin, M. Malešev, and I. Ignjatović. 2010. “Comparative environmental assessment of natural and recycled aggregate concrete.” Waste Manage. (Oxford) 30 (11): 2255–2264. https://doi.org/10.1016/j.wasman.2010.04.012.
Marquardt, D. W. 1963. “An algorithm for least-squares estimation of nonlinear parameters.” J. Soc. Ind. Appl. Math. 11 (2): 431–441. https://doi.org/10.1137/0111030.
Mastella, M. A., E. S. Gislon, F. Pelisser, C. Ricken, L. D. Silva, E. Angioletto, and O. R. K. Montedo. 2014. “Mechanical and toxicological evaluation of concrete artifacts containing waste foundry sand.” Waste Manage. (Oxford) 34 (8): 1495–1500. https://doi.org/10.1016/j.wasman.2014.02.001.
Mauricio, G. R., H. A. Juan, F. B. Javier, S. R. Eleazar, R. L. Isauro, C. S. Eduardo, E. R. C. Víctor, and C. L. Carmen. 2016. “Characterization of waste molding sands, for their possible use as building material.” In Characterization of minerals, metals, and materials, 615–621. Cham, Switzerland: Springer.
Mavroulidou, M., and D. Lawrence. 2019. “Can waste foundry sand fully replace structural concrete sand?” J. Mater. Cycles Waste Manage. 21 (3): 594–605. https://doi.org/10.1007/s10163-018-00821-1.
Milne, L. 1995. “Feature selection using neural networks with contribution measures.” In Proc., AI-Conf. Singapore: World Scientific.
Monosi, S., F. Tittarelli, C. Giosuè, and M. L. Ruello. 2013. “Effect of two different sources and washing treatment on the properties of UFS by-products for mortar and concrete production.” Constr. Build. Mater. 44 (Jul): 260–266. https://doi.org/10.1016/j.conbuildmat.2013.02.029.
Naderpour, H., A. H. Rafiean, and P. Fakharian. 2018. “Compressive strength prediction of environmentally friendly concrete using artificial neural networks.” J. Build. Eng. 16 (Mar): 213–219. https://doi.org/10.1016/j.jobe.2018.01.007.
Naik, T. R., V. M. Patel, D. M. Parikh, and M. P. Tharaniyil. 1994. “Utilization of used foundry sand in concrete.” J. Mater. Civ. Eng. 6 (2): 254–263. https://doi.org/10.1061/(ASCE)0899-1561(1994)6:2(254).
Nithya, M., A. K. Priya, R. Muthukumaran, and G. K. Arunvivek. 2017. “Properties of concrete containing waste foundry sand for partial replacement of fine aggregate in concrete.” Indian J. Eng. Mater. Sci. 24 (2): 162–166.
Pathariya Saraswati, C., K. Rana Jaykrushna, A. Shah Palas, and G. Mehta Jay. 2013. “Application of waste foundry sand for evolution of low-cost concrete.” Int. J. Eng. Trends Technol. 4 (10): 4281–4286.
Patil, R., P. Mehetre, and K. Phalak. 2015. “Development of concrete with partial replacement of fine aggregate by waste foundry sand.” In Vol. 2 of Proc., Int. Conf. on Recent Trends in Engineering and Techonology, 581–587. Paris: World Academy of Science, Engineering and Technology.
Prabhu, G. G., J. H. Hyun, and Y. Y. Kim. 2014. “Effects of foundry sand as a fine aggregate in concrete production.” Constr. Build. Mater. 70 (Nov): 514–521. https://doi.org/10.1016/j.conbuildmat.2014.07.070.
Pradhan, S., B. Tiwari, S. Kumar, and S. V. Barai. 2019. “Comparative LCA of recycled and natural aggregate concrete using particle packing method and conventional method of design mix.” J. Cleaner Prod. 228 (Aug): 679–691. https://doi.org/10.1016/j.jclepro.2019.04.328.
Resmi, R., S. Sabari Saravanan, S. Saravana Kumar, R. Vigneshwaran, and S. Prem Kumar. 2017. “Experimental study on mechanical properties of concrete incorporating waste foundry sand with destructive and non-destructive test.” In Proc., 6th National Conf. on Innovative Practices in Construction and Waste Management, 75–79. Coimbatore, Tamilnadu, India: Sri Ramakrishna Institute of Technology.
Rumelhart, D. E., G. E. Hinton, and R. J. Williams. 1986. “Learning representations by back-propagating errors.” Nature 323 (6088): 533. https://doi.org/10.1038/323533a0.
Salokhe, E. P., and D. Desai. 2014. “Application of foundry waste sand in manufacture of concrete.” IOSR J. Mech. Civ. Eng. 1684–2278.
Sandhu, R. K., and R. Siddique. 2019. “Strength properties and microstructural analysis of self-compacting concrete incorporating waste foundry sand.” Constr. Build. Mater. 225 (Nov): 371–383. https://doi.org/10.1016/j.conbuildmat.2019.07.216.
Sanikhani, H., R. C. Deo, Z. M. Yaseen, O. Eray, and O. Kisi. 2018. “Non-tuned data intelligent model for soil temperature estimation: A new approach.” Geoderma 330 (Nov): 52–64. https://doi.org/10.1016/j.geoderma.2018.05.030.
Sastry, K. G. K., A. Ravitheja, and T. C. S. Reddy. 2018. “Effect of foundry sand and mineral admixtures on mechanical properties of concrete.” Arch. Civ. Eng. 64 (1): 117–131. https://doi.org/10.2478/ace-2018-0008.
Siddique, R., Y. Aggarwal, P. Aggarwal, E.-H. Kadri, and R. Bennacer. 2011. “Strength, durability, and micro-structural properties of concrete made with used-foundry sand (UFS).” Constr. Build. Mater. 25 (4): 1916–1925. https://doi.org/10.1016/j.conbuildmat.2010.11.065.
Siddique, R., G. De Schutter, and A. Noumowe. 2009. “Effect of used-foundry sand on the mechanical properties of concrete.” Constr. Build. Mater. 23 (2): 976–980. https://doi.org/10.1016/j.conbuildmat.2008.05.005.
Siddique, R., and E.-H. Kadri. 2011. “Effect of metakaolin and foundry sand on the near surface characteristics of concrete.” Constr. Build. Mater. 25 (8): 3257–3266. https://doi.org/10.1016/j.conbuildmat.2011.03.012.
Siddique, R., and A. Noumowe. 2008. “Utilization of spent foundry sand in controlled low-strength materials and concrete.” Resour. Conserv. Recycl. 53 (1–2): 27–35. https://doi.org/10.1016/j.resconrec.2008.09.007.
Siddique, R., G. Singh, R. Belarbi, and K. Ait-Mokhtar. 2015. “Comparative investigation on the influence of spent foundry sand as partial replacement of fine aggregates on the properties of two grades of concrete.” Constr. Build. Mater. 83 (May): 216–222. https://doi.org/10.1016/j.conbuildmat.2015.03.011.
Siddique, R., G. Singh, and M. Singh. 2018. “Recycle option for metallurgical by-product (spent foundry sand) in green concrete for sustainable construction.” J. Cleaner Prod. 172 (Jan): 1111–1120. https://doi.org/10.1016/j.jclepro.2017.10.255.
Singh, G., and R. Siddique. 2012a. “Abrasion resistance and strength properties of concrete containing waste foundry sand (WFS).” Constr. Build. Mater. 28 (1): 421–426. https://doi.org/10.1016/j.conbuildmat.2011.08.087.
Singh, G., and R. Siddique. 2012b. “Effect of waste foundry sand (WFS) as partial replacement of sand on the strength, ultrasonic pulse velocity and permeability of concrete.” Constr. Build. Mater. 26 (1): 416–422. https://doi.org/10.1016/j.conbuildmat.2011.06.041.
Singh, G., and R. G. Siddique. 2013. “Strength and durability studies of concrete containing waste foundry sand.” Ph.D. dissertation, Dept. of Civil Engineering, Thapar Univ.
Smarzewski, P., and D. Barnat-Hunek. 2016. “Mechanical and durability related properties of high performance concrete made with coal cinder and waste foundry sand.” Constr. Build. Mater. 121 (Sep): 9–17. https://doi.org/10.1016/j.conbuildmat.2016.05.148.
Sowmya, M., and J. C. Kumar. 2015. “Mixing of waste foundry sand in concrete.” Int. J. Eng. Res. Sci. Technol. 4 (4): 322–335.
Tanyildizi, H. 2018. “Prediction of the strength properties of carbon fiber-reinforced lightweight concrete exposed to the high temperature using artificial neural network and support vector machine.” In Advances in civil engineering 2018. Cairo, Egypt: Hindawi.
Torres, A., L. Bartlett, and C. Pilgrim. 2017. “Effect of foundry waste on the mechanical properties of portland cement concrete.” Constr. Build. Mater. 135 (Mar): 674–681. https://doi.org/10.1016/j.conbuildmat.2017.01.028.
Tropsha, A., P. Gramatica, and V. K. Gombar. 2003. “The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models.” QSAR Comb. Sci. 22 (1): 69–77. https://doi.org/10.1002/qsar.200390007.
Verbeeck, H., R. Samson, F. Verdonck, and R. Lemeur. 2006. “Parameter sensitivity and uncertainty of the forest carbon flux model FORUG: A Monte Carlo analysis.” Tree Physiol. 26 (6): 807–817. https://doi.org/10.1093/treephys/26.6.807.
Walker, H. M. 1929. Studies in the history of statistical method: With special reference to certain educational problems. Philadelphia: Williams & Wilkins. https://doi.org/10.1037/13379-000.
Werbos, P. 1974. “Beyond regression: New tools for prediction and analysis in the behavioral sciences.” Ph.D. thesis, Dept. of Statistics, Harvard Univ.
Yang, Y., and Q. Zhang. 1997. “A hierarchical analysis for rock engineering using artificial neural networks.” Rock Mech. Rock Eng. 30 (4): 207–222. https://doi.org/10.1007/BF01045717.
Yaseen, Z. M., R. C. Deo, A. Hilal, A. M. Abd, L. C. Bueno, S. Salcedo-Sanz, and M. L. Nehdi. 2018. “Predicting compressive strength of lightweight foamed concrete using extreme learning machine model.” Adv. Eng. Software 115 (1): 112–125. https://doi.org/10.1016/j.advengsoft.2017.09.004.
Yuan, Z., L.-N. Wang, and X. Ji. 2014. “Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS.” Adv. Eng. Software 67 (Jan): 156–163. https://doi.org/10.1016/j.advengsoft.2013.09.004.
Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 33 • Issue 4 • April 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: May 19, 2020
Accepted: Aug 31, 2020
Published online: Jan 28, 2021
Published in print: Apr 1, 2021
Discussion open until: Jun 28, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.