Advances in concrete construction

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A novel approach for sustainable construction using Bi-LSTM-CNN based predictive analytics on ceramic waste powder for concrete

  • Ying Zhang (China Construction Fifth Engineering Bureau Haixi Investment and Construction Co., Ltd) ;
  • Tianshun Liu (China Construction Fifth Engineering Bureau Haixi Investment and Construction Co., Ltd) ;
  • Panfeng Guo (China Construction Fifth Engineering Bureau Haixi Investment and Construction Co., Ltd)
  • Received : 2023.10.01
  • Accepted : 2024.02.12
  • Published : 2024.07.25
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Abstract

The enormous volumes of waste produced by the building sector, particularly by the ceramics industry, which releases large amounts of CWP-wates of ceramic, through polishing and cutting procedures, causes serious environmental issues. Traditional methods of concrete production increase pollutions and create a severe burden on waste capacity. CWP usage in concrete mixtures as a responsible option, but however, accurate prediction is a one of the major problems. Current predictive models commonly create issues regarding accuracy, particularly by capturing the difficult, interactions among the mechanical features of concrete based CWP content. These drawbacks highlight the urgent need for an advanced predictive analytics model that can able to handle both environmental issues related to CWP concrete's manufacture also accurately predict the material's qualities. By considering this as a background, this study presents an effective novel hybrid solution for sustainable construction, which combines Deep convolutional particle filtering techniques (CNN-PF) with attention based Bi-LSM based predictive analytics on CWP for concrete. The proposed model combines the benefits of CNN-PF to extract important features in input data like composition details or microstructural images. Where the particle filtering improves the model's efficacy by handling abnormalities in the data. Additionally, attention based Bi-LSTM helps to capture long term dependencies in sequential data to improve prediction accuracy. The experimental investigation is performed with 54 different concrete mixes with two existing articles, in different Molds, using CWP. The outcomes highlight that our proposed model marks its remarkable outcomes than the existing models by achieving a notable result in both model-based prediction and Mold based prediction.

Keywords

References

  1. Abiodun, Y.O., Olanrewaju, O.A., Gbenebor, O.P., Ochulor, E.F., Obasa, D.V. and Adeosun, S.O. (2022), "Cutting cement industry CO2 emissions through metakaolin use in construction", Atmosphere, 13(9), 1494. https://doi.org/10.3390/atmos13091494
  2. Afrin, H., Huda, N. and Abbasi, R. (2021), "An overview of ecofriendly alternatives as the replacement of cement in concrete", IOP Conference Series: Materials Science and Engineering, Vol. 1200, No. 1, p. 012003. https://doi.org/10.1088/1757-899X/1200/1/012003
  3. Alabi, S.A., Arum, C., Adewuyi, A.P., Arum, R.C., Afolayan, J.O. and Mahachi, J. (2023), "Mathematical model for prediction of compressive strength of ternary blended cement concrete utilizing gene expression programming", Scientific African, 22, e01954. https://doi.org/10.1016/j.sciaf.2023.e01954
  4. Arunvivek, G., Maheswaran, G. and Kumar, S.S. (2015), "Ecofriendly solution to mitigate the toxic effects of hazardous construction industry waste by reusing in concrete for pollution control", Nature Environ. Pollut. Technol., 14(4), 963.
  5. Ayyasamy, R.K., Shaikh, F.B., Lah, N.S.B.C., Kalhoro, S., Chinnasamy, P. and Krisnan, S. (2023), "Industry 4.0 Digital Technologies and Information Systems: Implications for Manufacturing Firms Innovation Performance", Proceedings of 2023 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, pp. 1-6. https://doi.org/10.1109/ICCCI56745.2023.10128638
  6. Bou-Rabee, M.A., Naz, M.Y., Albalaa, I.E. and Sulaiman, S.A. (2022), "BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones", Energies, 15(6), 2226. https://doi.org/10.3390/en15062226
  7. Cao, J., Du, J., Fan, Q., Yang, J., Bao, C. and Liu, Y. (2024), "Reinforcement for earthquake-damaged glued-laminated timber knee-braced frames with self-tapping screws and CFRP fabric", Eng. Struct., 306, 117787. https://doi.org/10.1016/j.engstruct.2024.117787
  8. Chang, Q., Liu, L., Farooqi, M.U., Thomas, B. and Özkılıç, Y.O. (2023), "Data-driven based estimation of waste-derived ceramic concrete from experimental results with its environmental assessment", J. Mater. Res. Technol., 24, 6348-6368. https://doi.org/10.1016/j.jmrt.2023.04.223
  9. Chinnasamy, P., Sathya, K. B., Jebamani, B. J., Nithyasri, A. and Fowjiya, S. (2023), "Deep Learning: Algorithms, Techniques, and Applications — A Systematic Survey", In: L. Ashok Kumar, D. Karthika Renuka, & S. Geetha (Eds.), Deep Learning Research Applications for Natural Language Processing, pp. 1-17. https://doi.org/10.4018/978-1-6684-6001-6.ch001
  10. Dai, T., Fang, C., Liu, T., Zheng, S., Lei, G. and Jiang, G. (2024), "Waste glass powder as a high temperature stabilizer in blended oil well cement pastes: Hydration, microstructure and mechanical properties", Constr. Build. Mater., 439, 137359. https://doi.org/10.1016/j.conbuildmat.2024.137359
  11. El-Dieb, A.S., Taha, M.R. and Abu-Eishah, S.I. (2019), "Chapter 2 - The use of ceramic waste powder (CWP) in making ecofriendly concretes", Ceramic Mater.: Synth. Charact. Applicat. Recycl., pp. 1-35.
  12. Fang, B., Qian, Z., Song, Y., Diao, X., Shi, T., Cai, X. and Wang, L. (2024), "Evaluation of early crack resistance performance of concrete mixed with ternary minerals using temperature stress testing machine (TSTM)", J. Cleaner Product., 465, 142780. https://doi.org/10.1016/j.jclepro.2024.142780
  13. Ghasemi, M., Zhang, C., Khorshidi, H., Zhu, L. and Hsiao, P. (2023), "Seismic upgrading of existing RC frames with displacement-restraint cable bracing", Eng. Struct., 282, 115764. https://doi.org/10.1016/j.engstruct.2023.115764
  14. Guo, M., Huang, H., Zhang, W., Xue, C. and Huang, M. (2022), "Assessment of RC frame capacity subjected to a loss of corner column", J. Struct. Eng., 148(9), 04022122. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003423
  15. Hamad, M.A., Nasr, M., Shubbar, A., Al-Khafaji, Z., Al Masoodi, Z., Al-Hashimi, O., Kot, P., Alkhaddar, R. and Hashim, K. (2021), "Production of ultra-high-performance concrete with low energy consumption and carbon footprint using supplementary cementitious materials instead of silica fume: A review", Energies, 14(24), 8291. https://doi.org/10.3390/en14248291
  16. He, H., Shuang, E., Qiao, H., Yang, J., Lin, C., He, C. and Xu, P. (2024a), "A general and simple method to disperse 2D nanomaterials for promoting cement hydration", Constr. Build. Mater., 427, 136217. https://doi.org/10.1016/j.conbuildmat.2024.136217
  17. He, H., Shi, J., Yu, S., Yang, J., Xu, K., He, C. and Li, X. (2024b), "Exploring green and efficient zero-dimensional carbon-based inhibitors for carbon steel: From performance to mechanism", Constr. Build. Mater., 411, 134334. https://doi.org/10.1016/j.conbuildmat.2023.134334
  18. He, L., Chen, B., Liu, Q., Chen, H., Li, H., Chow, W.T., Tang, J., Du, Z., He, Y. and Pan, J. (2024c), "A quasi-exponential distribution of interfacial voids and its effect on the interlayer strength of 3D printed concrete", Addit. Manuf., 89, 104296. https://doi.org/10.1016/j.addma.2024.104296
  19. Huang, H., Guo, M., Zhang, W., Zeng, J., Yang, K. and Bai, H. (2021a), "Numerical investigation on the bearing capacity of RC columns strengthened by HPFL-BSP under combined loadings", J. Build. Eng., 39, 102266. https://doi.org/10.1016/j.jobe.2021.102266
  20. Huang, H., Yuan, Y., Zhang, W. and Zhu, L. (2021b), "Property assessment of high-performance concrete containing three types of fibers", Int. J. Concrete Struct. Mater., 15(1), 39. https://doi.org/10.1186/s40069-021-00476-7
  21. Huang, H., Li, M., Zhang, W. and Yuan, Y. (2022), "Seismic behavior of a friction-type artificial plastic hinge for the precast beam–column connection", Arch. Civil Mech. Eng., 22(4), 201. https://doi.org/10.1007/s43452-022-00526-1
  22. Huang, H., Li, M., Yuan, Y. and Bai, H. (2023), "Experimental research on the seismic performance of precast concrete frame with replaceable artificial controllable plastic hinges", J. Struct. Eng., 149(1), 04022222. https://doi.org/10.1061/JSENDH.STENG-11648
  23. Jie, Z. and Nan, C. (2020), "Concrete construction waste pollution and relevant prefabricated recycling measures", Nature Environ. Pollut. Technol., 19(1), 367-372.
  24. Kamath, M.V., Prashanth, S., Kumar, M. and Tantri, A. (2024), "Machine-Learning-Algorithm to predict the High-Performance concrete compressive strength using multiple data", J. Eng., Des Technol., 22(2), 532-560. https://doi.org/10.1108/JEDT-11-2021-0637
  25. Kanaan, D.M. (2016), "Recycling ceramic waste powder and its usage as an alternative ingredient in concrete", Theses, 356.
  26. Kashyap, R., Saxena, M., Gautam, A., Kushwaha, A., Priyanka, K., Patel, A. and Maurya, R.K. (2024), "Exploring sustainable construction through experimental analysis and AI predictive modelling of ceramic waste powder concrete", Asian J. Civil Eng., 25, 4789-4801. https://doi.org/10.1007/s42107-024-01080-2
  27. Kshirsagar, P.R., Upreti, K., Kushwah, V.S., Hundekari, S., Jain, D., Pandey, A.K. and Parashar, J. (2024), "Prediction and modeling of mechanical properties of concrete modified with ceramic waste using artificial neural network and regression model", Signal Image Video Process., 18, 183-197. https://doi.org/10.1007/s11760-024-03142-z
  28. Kumari, P., Paruthi, S., Alyaseen, A., Khan, A.H. and Jijja, A. (2024), "Predictive performance assessment of recycled coarse aggregate concrete using artificial intelligence: A review", Cleaner Materi., 13, 100263. https://doi.org/10.1016/j.clema.2024.100263
  29. Labaran, Y.H., Mathur, V.S. and Farouq, M.M. (2021), "The carbon footprint of construction industry: A review of direct and indirect emission", J. Sustain. Constr. Mater. Technol., 6(3), 101-115. https://doi.org/10.29187/jscmt.2021.66
  30. Lu, D., Ma, C., Du Xiuli, Jin, L. and Gong, Q. (2017), "Development of a new nonlinear unified strength theory for geomaterials based on the characteristic stress concept", Int. J. Geomech., 17(2), 04016058. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000729
  31. Lu, D., Liang, J., Du, X., Ma, C. and Gao, Z. (2019), "Fractional elastoplastic constitutive model for soils based on a novel 3D fractional plastic flow rule", Comput. Geotech., 105, 277-290. https://doi.org/10.1016/j.compgeo.2018.10.004
  32. Lu, D., Meng, F., Zhou, X., Zhuo, Y., Gao, Z. and Du, X. (2023), "A dynamic elastoplastic model of concrete based on a modeling method with environmental factors as constitutive variables", J. Eng. Mech., 149(12), 04023102. https://doi.org/10.1061/JENMDT.EMENG-7206
  33. Ma, Z., Hu, R., Shen, J., Wang, C. and Wu, H. (2023), "Chloride diffusion and binding capacity of sustainable cementitious materials with construction waste powder as cement replacement", Constr. Build. Mater., 368, 130352. https://doi.org/10.1016/j.conbuildmat.2023.130352
  34. Maddalena, R., Roberts, J.J. and Hamilton, A. (2018), "Can Portland cement be replaced by low-carbon alternative materials? A study on the thermal properties and carbon emissions of innovative cements", J. Cleaner Product., 186, 933-942. https://doi.org/10.1016/j.jclepro.2018.02.138
  35. Mozhdehi, R.J. and Medeiros, H. (2017), "Deep convolutional particle filter for visual tracking", Proceedings of 2017 IEEE International conference on image processing (ICIP), Beijing, China, September. https://doi.org/10.1109/ICIP.2017.8296963
  36. Nafees, A., Khan, S., Javed, M.F., Alrowais, R., Mohamed, A.M., Mohamed, A. and Vatin, N.I. (2022), "Forecasting the mechanical properties of plastic concrete employing experimental data using machine learning algorithms: DT, MLPNN, SVM, and RF", Polymers, 14(8), 1583. https://doi.org/10.3390/polym14081583
  37. Pham, A.-D., Ngo, N.-T., Nguyen, Q.-T. and Truong, N.-S. (2020), "Hybrid machine learning for predicting strength of sustainable concrete", Soft Comput., 24(19), 14965-14980. https://doi.org/10.1007/s00500-020-04848-1
  38. Ray, S., Haque, M., Rahman, M.M., Sakib, M.N. and Al Rakib, K. (2021), "Experimental investigation and SVM-based prediction of compressive and splitting tensile strength of ceramic waste aggregate concrete", J. King Saud Univ.-Eng. Sci., 36(2), 112-121. https://doi.org/10.1016/j.jksues.2021.08.010
  39. Rostami, A., Kurtayev, M. and Batikha, M. (2020), "Sustainable Concrete Production Using Ceramic Waste Powder (CWP)", In: Sustainable Development and Social Responsibility—Volume 1: Proceedings of the 2nd American University in the Emirates International Research Conference, AUEIRC'18, Dubai, UAE.
  40. Shen, W., Cao, L., Li, Q., Zhang, W., Wang, G. and Li, C. (2015), "Quantifying CO2 emissions from China's cement industry", Renew. Sustain. Energy Rev., 50, 1004-1012. https://doi.org/10.1016/j.rser.2015.05.031
  41. Song, H., Ahmad, A., Ostrowski, K.A. and Dudek, M. (2021), "Analyzing the compressive strength of ceramic waste-based concrete using experiment and artificial neural network (ANN) approach", Materials, 14(16), 4518. https://doi.org/10.3390/ma14164518
  42. Song, X., Wang, W., Deng, Y., Su, Y., Jia, F., Zaheer, Q. and Long, X. (2024), "Data-driven modeling for residual velocity of projectile penetrating reinforced concrete slabs", Eng. Struct., 306, 117761. https://doi.org/10.1016/j.engstruct.2024.117761
  43. Sumranwanich, T. and Tangtermsirikul, S. (2004), "A model for predicting time-dependent chloride binding capacity of cement fly ash cementitious system", Mater. Struct., 37, 387-396. https://doi.org/10.1007/BF02479635
  44. Sun, L., Wang, C., Zhang, C., Yang, Z., Li, C. and Qiao, P. (2023), "Experimental investigation on the bond performance of sea sand coral concrete with FRP bar reinforcement for marine environments", Adv. Struct. Eng., 26(3), 533-546. https://doi.org/10.1177/13694332221131153
  45. Wei, J., Ying, H., Yang, Y., Zhang, W., Yuan, H. and Zhou, J. (2023), "Seismic performance of concrete-filled steel tubular composite columns with ultra high performance concrete plates", Eng. Struct., 278, 115500. https://doi.org/10.1016/j.engstruct.2022.115500
  46. Yang, J., Jiang, P., Suhail, S.A., Sufian, M. and Deifalla, A.F. (2023), "Experimental investigation and AI prediction modelling of ceramic waste powder concrete–An approach towards sustainable construction", J. Mater. Res. Technol., 23, 3676-3696. https://doi.org/10.1016/j.jmrt.2023.02.024
  47. Zhang, W., Lin, J., Huang, Y., Lin, B. and Kang, S. (2024), "Temperature-dependent debonding behavior of adhesively bonded CFRP-UHPC interface", Compos. Struct., 340, 118200. https://doi.org/10.1016/j.compstruct.2024.118200
  48. Zhao, R., Li, C. and Guan, X. (2024), "Advances in Modeling Surface Chloride Concentrations in Concrete Serving in the Marine Environment: A Mini Review", Buildings, 14(6), 1879. https://doi.org/10.3390/buildings14061879
  49. Zheng, C., Zhang, H., Cai, X., Chen, L., Liu, M., Lin, H. and Wang, X. (2021), "Characteristics of CO2 and atmospheric pollutant emissions from China's cement industry: A life-cycle perspective", J. Cleaner Product., 282, 124533. https://doi.org/10.1016/j.jclepro.2020.124533