Compressive strength prediction of lightweight short columns at elevated temperature using gene expression programing and artificial neural network
Abstract
The experimental behavior of reinforced concrete elements exposed to fire is limited in the literature. Although there are few experimental programs that investigate the behavior of lightweight short columns, there is still a lack of formulation that can accurately predict their ultimate load at elevated temperature. Thus, new equations are proposed in this study to predict the compressive strength of the lightweight short column using Gene Expression Programming (GEP) and Artificial neural networks (ANN). A total of 83 data set is used to establish GEP and ANN models where 70% of the data are used for training and 30% of the data are used for validation and testing. The predicting variables are temperature, concrete compressive strength, steel yield strength, and spacing between stirrups. The developed models are compared with the ACI equation for short columns. The results have shown that the GEP and ANN models have a strong potential to predict the compressive strength of the lightweight short column. The predicted compressive strengths of short lightweight columns using the GEP and ANN models are closer to the experimental results than that obtained using the ACI equations.
Keyword : Gene expression programing, artificial neural network, lightweight concrete, short column, elevated temperature
How to Cite
Ashteyat, A., Obaidat, Y. T., Murad, Y. Z., & Haddad, R. (2020). Compressive strength prediction of lightweight short columns at elevated temperature using gene expression programing and artificial neural network. Journal of Civil Engineering and Management, 26(2), 189-199. https://doi.org/10.3846/jcem.2020.11931
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Benali, A., Boukhatem, B., Hussien, M. N., Nechnech, A., & Karray, M. (2017). Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach. Journal of Civil Engineering and Management, 23(3), 393–408. https://doi.org/10.3846/13923730.2016.1144643
Bogas, J. A. & Gomes, A. (2013). Compressive behavior and failure modes of structural lightweight aggregate concrete – characterization and strength prediction. Materials & Design, 46, 832–841. https://doi.org/10.1016/j.matdes.2012.11.004
Cascardi, A., Longo, F., Micelli, F., & Aiello, M. A. (2017). Compressive strength of confined column with fiber reinforced mortar (FRM): New design-oriented-models. Construction and Building Materials, 156, 387–401. https://doi.org/10.1016/j.conbuildmat.2017.09.004
Cevik, A., & Sonebi, M. (2008). Modelling the performance of self-compacting SIFCON of cement slurries using genetic programming technique. Computers and Concrete, 5(5), 475–490. https://doi.org/10.12989/cac.2008.5.5.475
Chithra, S., Kumar, S. R. R. S., Chinnaraju, K., & Ashmita, F. A. (2016). 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, 528–535. https://doi.org/10.1016/j.conbuildmat.2016.03.214
Cusson, D., & Paultre, P. (1995). Stress-strain model for confined high-strength concrete. Journal of Structural Engineering, 121(3), 468–477. https://doi.org/10.1061/(ASCE)0733-9445(1995)121:3(468)
Esfahani, M. R., & Kianoush, M. R. (2005). Axial compressive strength of reinforced concrete columns wrapped with fibre reinforced polymers (FRP). In International Conference “Repair and Renovation of Concrete Structures, University of Dundee, Scotland, UK. https://www.icevirtuallibrary.com/doi/abs/10.1680/rarocs.34051.0039
Farghal, O. A., & Diab, H. M. A. (2013). Prediction of axial compressive strength of reinforced concrete circular short columns confined with carbon fiber reinforced polymer wrapping sheets. Journal of Reinforced Plastics and Composites, 32(19), 1406–1418. https://doi.org/10.1177/0731684413499830
Ferreira, C. (2001). Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 13(2), 87–129.
Ferreira, C. (2002). Gene expression programming in problem solving. In R. Roy, M. Köppen, S. Ovaska, T. Furuhashi, & F. Hoffmann (Eds.), Soft computing and industry (pp. 635– 653). Springer London. https://doi.org/10.1007/978-1-4471-0123-9_54
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Gepsoft. (2014). Gepsoft GeneXproTools – Data modeling & (Analysis software). https://www.gepsoft.com/
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González-Taboada, I., González-Fonteboa, B., Martínez-Abella, F., & Pérez-Ordóñez, J. L. (2016). Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming. Construction and Building Materials, 106, 480–499. https://doi.org/10.1016/J.CONBUILDMAT.2015.12.136
Haddad, R. H., & Ashour, D. M. (2013). Thermal performance of steel fibrous lightweight aggregate concrete short columns. Journal of Composite Materials, 47(16), 2013–2025. https://doi.org/10.1177/0021998312453605
Jafari, S., & Mahini, S. S. (2017). Lightweight concrete design using Gene expression programing. Construction and Building Materials, 139, 93–100. https://doi.org/10.1016/j.conbuildmat.2017.01.120
Kayali, O. (2008). Fly ash lightweight aggregates in high performance concrete. Construction and Building Materials, 22(12), 2393–2399. https://doi.org/10.1016/j.conbuildmat.2007.09.001
Li, Y., Cao, S., & Jing, D. (2018). Concrete columns reinforced with high-strength steel subjected to reversed cycle loading. ACI Structural Journal, 115(4), 1037–1048. https://doi.org/10.14359/51701296
Lim, J. C., Karakus, M., & Ozbakkaloglu, T. (2016). Evaluation of ultimate conditions of FRP-confined concrete columns using genetic programming. Computers & Structures, 162, 28–37. https://doi.org/10.1016/j.compstruc.2015.09.005
Mander, J. B., Priestley, M. J. N., & Park, R. (1988). Theoretical stress‐strain model for confined concrete. Journal of Structural Engineering, 114(8), 1804–1826. https://doi.org/10.1061/(ASCE)0733-9445(1988)114:8(1804)
Mostofinejad, D., & Moshiri, N. (2015). Compressive strength of CFRP composites used for strengthening of RC columns: Comparative evaluation of EBR and grooving methods. Journal of Composites for Construction, 19(5), 04014079. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000545
Mousavi, S. M., Aminian, P., Gandomi, A. H., Alavi, A. H., & Bolandi, H. (2012). A New predictive model for compressive strength of HPC using Gene expression programming. Advances in Engineering Software, 45(1), 105–114. https://doi.org/10.1016/j.advengsoft.2011.09.014
Murad, Y., Ashteyat, A., & Hunaifat, R. (2019a). Predictive model to the bond strength of FRP-to-concrete under direct pullout using Gene expression programming. Journal of Civil Engineering and Management, 25(8), 773–784. https://doi.org/10.3846/jcem.2019.10798
Murad, Y., Imam, R., Abu Hajar, H., Habeh, D., Hammad, A., & Shawash, Z. (2019b). Predictive compressive strength models for green concrete. International Journal of Structural Integrity. https://doi.org/10.1108/IJSI-05-2019-0044
Naderpour, H., & Mirrashid, M. (2018). An innovative approach for compressive strength estimation of mortars having calcium inosilicate minerals. Journal of Building Engineering, 19, 205–215. https://doi.org/10.1016/j.jobe.2018.05.012
Nazari, A., & Torgal, F. P. (2013). Modeling the compressive strength of geopolymeric binders by Gene expression programming-GEP. Expert Systems with Applications, 40(14), 5427–5438. https://doi.org/10.1016/j.eswa.2013.04.014
Obaidat, Y. T., & Haddad, R. H. (2016). Prediction of residual mechanical behavior of heat-exposed LWAC short column: A NLFE model. Structural Engineering and Mechanics, 57(2), 265–280. https://doi.org/10.12989/sem.2016.57.2.265
Özcan, F. (2012). Gene expression programming based formulations for splitting tensile strength of concrete. Construction and Building Materials, 26(1), 404–410. https://doi.org/10.1016/j.conbuildmat.2011.06.039
Príncipe, J. C., Euliano, N. R., & Lefebvre, W. C. (1999). Neural and adaptive systems: Fundamentals through simulations. Wiley.
Saatcioglu, M., & Razvi, S. R. (1992). Strength and ductility of confined concrete. Journal of Structural Engineering, 118(6), 1590–1607. https://doi.org/10.1061/(ASCE)0733-9445(1992)118:6(1590)
Sarıdemir, M. (2010). Genetic programming approach for prediction of compressive strength of concretes containing rice husk ash. Construction and Building Materials, 24(10), 1911–1919. https://doi.org/10.1016/j.conbuildmat.2010.04.011
Seifi, M., Noorzaei, J., Jaafar, M. S., & Thanoon, W. A. (2008). Enhancements in idealized capacity curve generation for reinforced concrete regular framed structures subjected to seismic loading. Journal of Civil Engineering and Management, 14(4), 251–262. https://doi.org/10.3846/1392-3730.2008.14.24
Shahin, M. A., Jaksa, M. B., & Maier, H. R. (2009). Recent advances and future challenges for artificial neural systems in geotechnical engineering applications. Advances in Artificial Neural Systems, Article ID 308239. https://doi.org/10.1155/2009/308239
Shahrara, N., Çelik, T., & Gandomi, A. H. (2017). Gene expression programming approach to cost estimation formulation for utility projects. Journal of Civil Engineering and Management, 23(1), 85–95. https://doi.org/10.3846/13923730.2016.1210214
Sheikh, S. A., & Uzumeri, S. M. (1980). Strength and ductility of tied concrete columns. Journal of the Structural Division, 106(5), 1079–1102.
Soleimani, S., Rajaei, S., Jiao, P., Sabz, A., & Soheilinia, S. (2018). New prediction models for unconfined compressive strength of geopolymer stabilized soil using Multi-Gen genetic programming. Measurement, 113, 99–107. https://doi.org/10.1016/j.measurement.2017.08.043
Sonebi, M., & Cevik, A. (2009). Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash. Construction and Building Materials, 23(7), 2614–2622. https://doi.org/10.1016/j.conbuildmat.2009.02.012
Sturm, R. D., McAskill, N., Burg, R. G., & Morgan, D. R. (2000). Evaluation of lightweight concrete perfomance in 55 to 80 year-old ships. Materials Science, 189, 101–120. https://doi.org/10.14359/5848
Tanyildizi, H., & Çevik, A. (2010). Modeling mechanical performance of lightweight concrete containing silica fume exposed to high temperature using genetic programming. Construction and Building Materials, 24(12), 2612–2618. https://doi.org/10.1016/j.conbuildmat.2010.05.001
Wu, T., Wei, H., Zhang, Y. & Liu, X. (2018). Axial compressive behavior of lightweight aggregate concrete columns confined with transverse steel reinforcement. Advances in Mechanical Engineering, 10(3). https://doi.org/10.1177/1687814018766632
Al-Thairy, H. (2015). Effect of transverse reinforcement on the axial compressive strenght of reinforced concrete columns. Al-Qadisiyah Journal For Engineering Sciences, 9(1), 119–134.
Anilkumar, & Kumar, A. (2016). Experimental investigations on structural lightweight concrete columns obtained by blending of light weight aggregates. International Research Journal of Engineering and Technology (IRJET), 3(9), 1395–1401.
ANSYS. (2008). https://www.ansys.com/
Ashteyat, A. M., & Ismeik, M. (2018). Predicting residual compressive strength of self-compacted concrete under various temperatures and relative humidity conditions by artificial neural networks. Computers and Concrete, 21(1), 47–54. https://doi.org/10.12989/CAC.2018.21.1.047
Beheshti, A. S. B., Ketabdari, H., & Gharebaghi, S. A. (2017). Estimating shear strength of short rectangular reinforced concrete columns using nonlinear regression and Gene expression programming. Structures, 12, 13–23. https://doi.org/10.1016/j.istruc.2017.07.002
Benali, A., Boukhatem, B., Hussien, M. N., Nechnech, A., & Karray, M. (2017). Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach. Journal of Civil Engineering and Management, 23(3), 393–408. https://doi.org/10.3846/13923730.2016.1144643
Bogas, J. A. & Gomes, A. (2013). Compressive behavior and failure modes of structural lightweight aggregate concrete – characterization and strength prediction. Materials & Design, 46, 832–841. https://doi.org/10.1016/j.matdes.2012.11.004
Cascardi, A., Longo, F., Micelli, F., & Aiello, M. A. (2017). Compressive strength of confined column with fiber reinforced mortar (FRM): New design-oriented-models. Construction and Building Materials, 156, 387–401. https://doi.org/10.1016/j.conbuildmat.2017.09.004
Cevik, A., & Sonebi, M. (2008). Modelling the performance of self-compacting SIFCON of cement slurries using genetic programming technique. Computers and Concrete, 5(5), 475–490. https://doi.org/10.12989/cac.2008.5.5.475
Chithra, S., Kumar, S. R. R. S., Chinnaraju, K., & Ashmita, F. A. (2016). 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, 528–535. https://doi.org/10.1016/j.conbuildmat.2016.03.214
Cusson, D., & Paultre, P. (1995). Stress-strain model for confined high-strength concrete. Journal of Structural Engineering, 121(3), 468–477. https://doi.org/10.1061/(ASCE)0733-9445(1995)121:3(468)
Esfahani, M. R., & Kianoush, M. R. (2005). Axial compressive strength of reinforced concrete columns wrapped with fibre reinforced polymers (FRP). In International Conference “Repair and Renovation of Concrete Structures, University of Dundee, Scotland, UK. https://www.icevirtuallibrary.com/doi/abs/10.1680/rarocs.34051.0039
Farghal, O. A., & Diab, H. M. A. (2013). Prediction of axial compressive strength of reinforced concrete circular short columns confined with carbon fiber reinforced polymer wrapping sheets. Journal of Reinforced Plastics and Composites, 32(19), 1406–1418. https://doi.org/10.1177/0731684413499830
Ferreira, C. (2001). Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 13(2), 87–129.
Ferreira, C. (2002). Gene expression programming in problem solving. In R. Roy, M. Köppen, S. Ovaska, T. Furuhashi, & F. Hoffmann (Eds.), Soft computing and industry (pp. 635– 653). Springer London. https://doi.org/10.1007/978-1-4471-0123-9_54
Gandomi, A. H., Alavi, A. H., Kazemi, S., & Gandomi, M. (2014). Formulation of shear strength of slender RC beams using Gene expression programming, Part I: Without shear reinforcement. Automation in Construction, 42, 112–121. https://doi.org/10.1016/J.AUTCON.2014.02.007
Gepsoft. (2014). Gepsoft GeneXproTools – Data modeling & (Analysis software). https://www.gepsoft.com/
Gholampour, A., Gandomi, A. H., & Ozbakkaloglu, T. (2017). New formulations for mechanical properties of recycled aggregate concrete using Gene expression programming. Construction and Building Materials, 130, 122–145. https://doi.org/10.1016/J.CONBUILDMAT.2016.10.114
González-Taboada, I., González-Fonteboa, B., Martínez-Abella, F., & Pérez-Ordóñez, J. L. (2016). Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming. Construction and Building Materials, 106, 480–499. https://doi.org/10.1016/J.CONBUILDMAT.2015.12.136
Haddad, R. H., & Ashour, D. M. (2013). Thermal performance of steel fibrous lightweight aggregate concrete short columns. Journal of Composite Materials, 47(16), 2013–2025. https://doi.org/10.1177/0021998312453605
Jafari, S., & Mahini, S. S. (2017). Lightweight concrete design using Gene expression programing. Construction and Building Materials, 139, 93–100. https://doi.org/10.1016/j.conbuildmat.2017.01.120
Kayali, O. (2008). Fly ash lightweight aggregates in high performance concrete. Construction and Building Materials, 22(12), 2393–2399. https://doi.org/10.1016/j.conbuildmat.2007.09.001
Li, Y., Cao, S., & Jing, D. (2018). Concrete columns reinforced with high-strength steel subjected to reversed cycle loading. ACI Structural Journal, 115(4), 1037–1048. https://doi.org/10.14359/51701296
Lim, J. C., Karakus, M., & Ozbakkaloglu, T. (2016). Evaluation of ultimate conditions of FRP-confined concrete columns using genetic programming. Computers & Structures, 162, 28–37. https://doi.org/10.1016/j.compstruc.2015.09.005
Mander, J. B., Priestley, M. J. N., & Park, R. (1988). Theoretical stress‐strain model for confined concrete. Journal of Structural Engineering, 114(8), 1804–1826. https://doi.org/10.1061/(ASCE)0733-9445(1988)114:8(1804)
Mostofinejad, D., & Moshiri, N. (2015). Compressive strength of CFRP composites used for strengthening of RC columns: Comparative evaluation of EBR and grooving methods. Journal of Composites for Construction, 19(5), 04014079. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000545
Mousavi, S. M., Aminian, P., Gandomi, A. H., Alavi, A. H., & Bolandi, H. (2012). A New predictive model for compressive strength of HPC using Gene expression programming. Advances in Engineering Software, 45(1), 105–114. https://doi.org/10.1016/j.advengsoft.2011.09.014
Murad, Y., Ashteyat, A., & Hunaifat, R. (2019a). Predictive model to the bond strength of FRP-to-concrete under direct pullout using Gene expression programming. Journal of Civil Engineering and Management, 25(8), 773–784. https://doi.org/10.3846/jcem.2019.10798
Murad, Y., Imam, R., Abu Hajar, H., Habeh, D., Hammad, A., & Shawash, Z. (2019b). Predictive compressive strength models for green concrete. International Journal of Structural Integrity. https://doi.org/10.1108/IJSI-05-2019-0044
Naderpour, H., & Mirrashid, M. (2018). An innovative approach for compressive strength estimation of mortars having calcium inosilicate minerals. Journal of Building Engineering, 19, 205–215. https://doi.org/10.1016/j.jobe.2018.05.012
Nazari, A., & Torgal, F. P. (2013). Modeling the compressive strength of geopolymeric binders by Gene expression programming-GEP. Expert Systems with Applications, 40(14), 5427–5438. https://doi.org/10.1016/j.eswa.2013.04.014
Obaidat, Y. T., & Haddad, R. H. (2016). Prediction of residual mechanical behavior of heat-exposed LWAC short column: A NLFE model. Structural Engineering and Mechanics, 57(2), 265–280. https://doi.org/10.12989/sem.2016.57.2.265
Özcan, F. (2012). Gene expression programming based formulations for splitting tensile strength of concrete. Construction and Building Materials, 26(1), 404–410. https://doi.org/10.1016/j.conbuildmat.2011.06.039
Príncipe, J. C., Euliano, N. R., & Lefebvre, W. C. (1999). Neural and adaptive systems: Fundamentals through simulations. Wiley.
Saatcioglu, M., & Razvi, S. R. (1992). Strength and ductility of confined concrete. Journal of Structural Engineering, 118(6), 1590–1607. https://doi.org/10.1061/(ASCE)0733-9445(1992)118:6(1590)
Sarıdemir, M. (2010). Genetic programming approach for prediction of compressive strength of concretes containing rice husk ash. Construction and Building Materials, 24(10), 1911–1919. https://doi.org/10.1016/j.conbuildmat.2010.04.011
Seifi, M., Noorzaei, J., Jaafar, M. S., & Thanoon, W. A. (2008). Enhancements in idealized capacity curve generation for reinforced concrete regular framed structures subjected to seismic loading. Journal of Civil Engineering and Management, 14(4), 251–262. https://doi.org/10.3846/1392-3730.2008.14.24
Shahin, M. A., Jaksa, M. B., & Maier, H. R. (2009). Recent advances and future challenges for artificial neural systems in geotechnical engineering applications. Advances in Artificial Neural Systems, Article ID 308239. https://doi.org/10.1155/2009/308239
Shahrara, N., Çelik, T., & Gandomi, A. H. (2017). Gene expression programming approach to cost estimation formulation for utility projects. Journal of Civil Engineering and Management, 23(1), 85–95. https://doi.org/10.3846/13923730.2016.1210214
Sheikh, S. A., & Uzumeri, S. M. (1980). Strength and ductility of tied concrete columns. Journal of the Structural Division, 106(5), 1079–1102.
Soleimani, S., Rajaei, S., Jiao, P., Sabz, A., & Soheilinia, S. (2018). New prediction models for unconfined compressive strength of geopolymer stabilized soil using Multi-Gen genetic programming. Measurement, 113, 99–107. https://doi.org/10.1016/j.measurement.2017.08.043
Sonebi, M., & Cevik, A. (2009). Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash. Construction and Building Materials, 23(7), 2614–2622. https://doi.org/10.1016/j.conbuildmat.2009.02.012
Sturm, R. D., McAskill, N., Burg, R. G., & Morgan, D. R. (2000). Evaluation of lightweight concrete perfomance in 55 to 80 year-old ships. Materials Science, 189, 101–120. https://doi.org/10.14359/5848
Tanyildizi, H., & Çevik, A. (2010). Modeling mechanical performance of lightweight concrete containing silica fume exposed to high temperature using genetic programming. Construction and Building Materials, 24(12), 2612–2618. https://doi.org/10.1016/j.conbuildmat.2010.05.001
Wu, T., Wei, H., Zhang, Y. & Liu, X. (2018). Axial compressive behavior of lightweight aggregate concrete columns confined with transverse steel reinforcement. Advances in Mechanical Engineering, 10(3). https://doi.org/10.1177/1687814018766632