EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 497 (2024) E3S Web Conf., 497 (2024) 02011 References
Open Access
Issue
E3S Web Conf.
Volume 497, 2024
5th International Conference on Energetics, Civil and Agricultural Engineering (ICECAE 2024)
Article Number 02011
Number of page(s) 12
Section Civil Engineering
DOI https://doi.org/10.1051/e3sconf/202449702011
Published online 07 March 2024
  1. Abba, S. I., Usman, A. G., & Isik, S. Simulation for response surface in the HPLC optimization method development using artificial intelligence models: A data-driven approach. Chemometrics and Intelligent Laboratory Systems 201, 104007 (2020) [CrossRef] [Google Scholar]
  2. Abdullahi, H. U., Usman, A. G., & Abba, S. I. Modelling the Absorbance of a Bioactive Compound in HPLC Method using Artificial Neural Network and Multilinear Regression Methods. Dutse Journal of Pure and Applied Sciences (DUJOPAS) 6(2), 362–371 (2020) [Google Scholar]
  3. Adamu M, Haruna S.I., Malami S.I., Ibrahim M.N., Abba S.I., Ibrahim Y.E. Prediction of compressive strength of concrete incorporated with jujube seed as partial replacement of coarse aggregate: a feasibility of Hammerstein–Wiener model versus support vector machine. Model. Earth Syst. Environ. 8, 3435–3445 (2022) [CrossRef] [Google Scholar]
  4. Akpinar, P., & Uwanuakwa, I. D. Investigation of the parameters influencing progress of concrete carbonation depth by using artificial neural networks. Materiales de Construccion 70(337), 1–14 (2020) [Google Scholar]
  5. Akpinar, Pinar, & Uwanuakwa, I.D. Methodology 9(2), 99–108 (2016) [Google Scholar]
  6. Alas, M., Ismael, S., Ali, A., Ph, D., Asce, A. M., Abdulhadi, Y. Experimental Evaluation and Modeling of Polymer Nanocomposite Modified Asphalt Binder Using ANN and ANFIS. Journal of Materials in Civil Engineering 32(10), 1–11 (2020) [CrossRef] [Google Scholar]
  7. Baba, N. M., M. Makhtar, Abdullah, S. and Awang, M. K. Current issues in ensemble methods and its applications. Computer Science, Mathematics 81(2), 266–276 (2015) [Google Scholar]
  8. Choi, J., Lee, Y., Yong, Y., & Yeon, B. Image-processing technique to detect carbonation regions of concrete sprayed with a phenolphthalein solution. Construction and Building Materials 154, 451–461 (2017) [CrossRef] [Google Scholar]
  9. Costache, R., Pham, Q. B., Sharifi, E., Linh, N. T. T., Abba, S. I., Vojtek, M., … Khoi, D. N. (2020). Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and GIS techniques. Remote Sensing 12(1), 106 (2020) [Google Scholar]
  10. Dede, T., Kankal, M., Vosoughi, A. R., Grzywiński, M., & Kripka, M. (2019). Artificial Intelligence Applications in Civil Engineering. Advances in Civil Engineering 2019, 8384523 (2019) [Google Scholar]
  11. Elkiran, G., Nourani, V. and Abba, S. Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. Journal of Hydrology 577, 123962 (2019) [CrossRef] [Google Scholar]
  12. EN 13791, Assessment of in-situ compressive strength in structures and precast concrete components, BSI, London (2007) [Google Scholar]
  13. Fathima, J., Mangai, A. and Gulyani, B. B. An ensemble method for predicting biochemical oxygen demand in river water using data mining techniques, Int. J. River Basin Manag. 12(4) 357–366 (2014) [CrossRef] [Google Scholar]
  14. Felix, E. F., Possan, E., & Carrazedo, R. (2019). Analysis of training parameters in the ANN learning process to mapping the concrete carbonation depth. Journal of Building Pathology and Rehabilitation 4, 16 (2019) [CrossRef] [Google Scholar]
  15. Granata, F., Papirio, S., Esposito, G., Gargano, R., & de Marinis, G. Machine learning algorithms for the forecasting of wastewater quality indicators. Water 9(2), 1–12 (2017) [Google Scholar]
  16. Gulyani, B. B. and Fathima, A. Introducing Ensemble Methods to Predict the Performance of Waste Water Treatment Plants (WWTP), Int. J. Environ. Sci. Dev. 8(7), 501-506 (2017) [CrossRef] [Google Scholar]
  17. Haruna S.I., Malami S.I., Adamu M., Usman A. G., Farouk A.I.B., Ali S.I.A. , (2021) Compressive strength of self-compacting concrete modified with rice husk ash and calcium carbide waste modeling: A Feasibility of Emotional intelligent model (EANN) versus traditional FFNN, Arabian Journal for Science and Engineering 46, 11207–11222 (2021) [CrossRef] [Google Scholar]
  18. Houseen, Q. and Akpınar, P. Evaluation of carbonation depth evolution tendencies of reinforced concrete buildings located in coastal and inland areas of north Cyprus. IOP Conference Series: Materials Science and Engineering 800, 012023 (2020) [CrossRef] [Google Scholar]
  19. Khashman, A. and Akpinar, P. Non-Destructive Prediction of Concrete Compressive Strength Using Neural Net-works. Procedia Computer Science 108, 2358-2362 (2017) [CrossRef] [Google Scholar]
  20. Lee, H., Lee, H. S., and Suraneni, P. Evaluation of carbonation progress using AIJ model, FEM analysis, and machine learning algorithms. Construction and Building Materials 259, 119703 (2020) [CrossRef] [Google Scholar]
  21. Lu, C., & Liu, R. Predicting Carbonation Depth of Prestressed Concrete under Different Stress States Using Artificial Neural Network. Advances in Artificial Neural Systems 2009, 1–8 (2009) [CrossRef] [Google Scholar]
  22. Lu, P., Chen, S., & Zheng, Y. Artificial intelligence in civil engineering. Mathematical Problems in Engineering 2012, 1–23 (2012) [Google Scholar]
  23. Malami S.I., Anwar F.H. ; Abdulrahman S. ; Haruna S.I. ; Ali S.I. A, Abba S.I. , Implementation of hybrid neuro-fuzzy and self-turning predictive model for the prediction of concrete carbonation depth: a soft computing technique, Re-sults in Engineering 10, 100228 (2021) [Google Scholar]
  24. Malami S.I., Musa A. A., Haruna S.I., Aliyu U. U., Usman A. G., Abdurrahman M. I., Bashir A., Abba S. I., Imple-mentation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste. Modelling Earth Systems and Environment 8, 1933–1947 (2022) [CrossRef] [Google Scholar]
  25. Malami S. I., Akpinar P. and Lawan M. (2018) Preliminary investigation of carbonation problem progress in concrete buildings of north Cyprus. MATEC Web Conf. 203, 06007 (2018) [CrossRef] [EDP Sciences] [Google Scholar]
  26. Paul, S. C., Panda, B., Huang, Y., Garg, A., & Peng, X. An empirical model design for evaluation and estimation of carbonation depth in concrete. Measurement: Journal of the International Measurement Confederation 124, 205–210 (2018) [CrossRef] [Google Scholar]
  27. Pham, Q. B., Abba, S. I., Usman, A. G., Linh, N. T. T., Gupta, V., Malik, A., … Tri, D. Q. Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall. Water Resources Management 33(15), 5067–5087 (2019) [CrossRef] [Google Scholar]
  28. Qiu, Q. A state-of-the-art review on the carbonation process in cementitious materials: Fundamentals and character-ization techniques. Construction and Building Materials 247, 118503 (2020) [CrossRef] [Google Scholar]
  29. Romero, N., Dupuy, C., & Quiñones, J. Revista ALCONPAT. Alconpat 7, 186–199 (2017) [CrossRef] [Google Scholar]
  30. Sharghi, E., Nourani, V. and Behfar, N. (2018). Earthfill dam seepage analysis using ensemble artificial intelligence based modeling. Journal of Hydroinformatics 20(5), 1071–1084 (2018) [CrossRef] [Google Scholar]
  31. Standard, B. Testing hardened concrete. Compressive Strength of Test Specimens, BS EN, 12390–12393 (2009) [Google Scholar]
  32. Taffese, W. Z., Sistonen, E., & Puttonen, J. CaPrM: Carbonation prediction model for reinforced concrete using machine learning methods. Construction and Building Materials 100, 70–82 (2015) [CrossRef] [Google Scholar]
  33. Usman, A. G., Işik, S., & Abba, S. I. A Novel Multi-model Data-Driven Ensemble Technique for the Prediction of Retention Factor in HPLC Method Development. Chromatographia 83(8), 933–945 (2020) [CrossRef] [Google Scholar]
  34. Vakhshouri, B. and Nejadi, S. Predicition of Compressive Strength in Light-Weight Self-Compacting Concrete by ANFIS Analytical Model, Arch. Civ. Eng. 61(2), 53–72 (2015) [CrossRef] [Google Scholar]
  35. Vapnik, V. The nature of statistical learning theory, Springer, New York (1995) [CrossRef] [Google Scholar]
  36. Vogler, N., Lindemann, M., Drabetzki, P., & Kühne, H. Alternative pH-indicators for determination of carbonation depth on cement-based concretes. Cement and Concrete Composites 109, 103565 (2020) [CrossRef] [Google Scholar]
  37. Wang, W. chuan, Chau, K. wing, Qiu, L., & Chen, Y. Bo. Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition. Environmental Research 139, 46–54 (2015) [CrossRef] [PubMed] [Google Scholar]
  38. Wu, Y., G. Li, Y. Yang, and T. An, Pollution evaluation and health risk assessment of airborne toxic metals in both indoors and outdoors of the Pearl River Delta, China, Environ. Res. 179, 108793 (2019) [CrossRef] [Google Scholar]
  39. Yaseen, Z. M., Faris, H. and Al-Ansari, N. Hybridized Extreme Learning Machine Model with Salp Swarm Algo-rithm: A Novel Predictive Model for Hydrological Application, Complexity 2020, 8206245 (2020) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.