457
Views
0
CrossRef citations to date
0
Altmetric
Civil & Environmental Engineering

Historical structure design method through data analysis and soft programming

ORCID Icon, &
Article: 2220499 | Received 08 Jan 2023, Accepted 29 May 2023, Published online: 05 Jun 2023

References

  • Abd Elhamed, A., Alkhatib, S., & Abdelfattah, A. M. H. (2022). Prediction of blast-induced structural response and associated damage using machine learning. Buildings, 12(12), 2093. 2093. https://doi.org/10.3390/buildings12122093
  • Ademovi ́c, N., Toholj, M., Radonic, D., Casarin, F., Komesar, S., & Ugarkovi ́c, K. (2022). Post-earthquake assessment and strengthening of a cultural-heritage residential masonry building after the 2020 Zagreb earthquake. Buildings, 12(11), 2024. 2024. https://doi.org/10.3390/buildings12112024
  • Agrawal, R. (2022). Sustainable design guidelines for additive manufacturing applications. Rapid Prototyping Journal, 28(7), 1221–12. https://doi.org/10.1108/RPJ-09-2021-0251
  • Akande, K. O., Owolabi, T. O., Twaha, S., & Olatunji, S. O. (2014). Performance comparison of SVM and ANN in predicting compressive strength of concrete. IOSR Journal of Computer Engineering, 16(5), 88–94. https://doi.org/10.9790/0661-16518894
  • Akter, S., Ali, R. M. E., Karim, S., Khatun, M., & Alam, M. F. (2018). Geomorphological, geological and engineering geological aspects for sustainable urban planning of Mymensingh City, Bangladesh. Open Journal of Geology, 8(7), 737–752. https://doi.org/10.4236/ojg.2018.87043
  • AlAlaween, W., Abueed, O., Gharaibeh, B., Alalawin, A., Mahfouf, M., Alsoussi, A., & Albashabsheh, N. (2022). The development of a radial based integrated network for the modelling of 3D fused deposition. Rapid Prototyping Journal, 29(2), 408–421. https://doi.org/10.1108/RPJ-04-2022-0121
  • Bagheri Poor, A., & Bolkhari Ghehi, H. (2018). Concepts analysis of religious inscriptions of goharshad Mosque according to educational & moral concepts (A case study of maqsurah verand). International Journal of Ar, 40–46. https://doi.org/10.5923/j.arts.20180802.0
  • Chopra, P., KumarSharma, R., & Kumar, M. (2016). Prediction of compressive strength of concrete using artificial neural network and genetic programming. Hindawi Publishing Corporation Advances in Materials Science and Engineering, 2016, 1–10. https://doi.org/10.1155/2016/7648467
  • Erdal, H. I. (2013). Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Engineering Applications of Artificial Intelligence, 26(7), 1689–1697. https://doi.org/10.1016/j.engappai.2013.03.014
  • Farooq, F. F., Czarnecki, S., Niewiadomski, P., Aslam, F., Alabduljabbar, H., Ostrowski, K. A., ´sliwa-Wieczorek, K., Nowobilski, T., & Malazdrewicz, S. (2021). A comparative study for the prediction of the compressive strength of self-compacting concrete modified with fly ash. Materials, 14(17), 4934. https://doi.org/10.3390/ma14174934
  • Farshad, M. (1977). On the shape of momentless tensionless masonry Domes. Building & Environment, 12(2), 81–85. https://doi.org/10.1016/0360-1323(77)90036-1
  • Fl ̈ory, S., & Pottmann, H. (2010). Ruled surfaces for rationalization and design in architecture. LIFE In Formation On Responsive Information and Variations in Architecture, 103–109.
  • Hasanzadeh, A., Vatin, N. I., Hematibahar, M., Kharun, M., & Shooshpasha, I. (2022). Prediction of the mechanical properties of basalt fiber reinforced high-performance concrete using machine learning techniques. Materials, 15(20), 7165. https://doi.org/10.3390/ma15207165
  • Hejazi, M., Baranizadeh, S., & Daei, M. (2021). Performance of Persian brick masonry single-shell domes subjected to uniform pressure and concentrated load. Structures, 34, 1710–1719. https://doi.org/10.1016/j.istruc.2021.08.100
  • Hematibahar, M., Vatin, N. I., Alaraza, H. A. A., Khalilavi, A., & Kharun, M. (2022). The prediction of compressive strength and compressive stress–strain of basalt fiber reinforced high-performance concrete using classical programming and logistic map algorithm. Materials, 19(19), 6975. https://doi.org/10.3390/ma15196975
  • Khan, M. A., Memon, S. A., Farooq, F., Javed, M. F., Aslam, F., & Alyousef, R. (2021). Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. Advances in Civil Engineering, 2021. https://doi.org/10.1155/2021/6618407
  • Khorasani, M., Loy, J., Ghasemi, A., Sharabian, E., Leary, M., Mirafzal, H., Cochrane, P., Rolfe, B., & Gibson, L. (2022). A review of industry 4.0 and additive manufacturing synergy. Rapid Prototyping Journal, 28(8), 1462–1475. https://doi.org/10.1108/RPJ-08-2021-0194
  • Latifi, R., Hadzima-Nyarko, M., Radu, D., & Rouhi, R. (2023). A brief overview on crack patterns, repair and strengthening of historical masonry structures. Materials, 16(5), 1882. 1882. https://doi.org/10.3390/ma16051882
  • McCormac, J., & Nelson, J. (2005). Design of reinforced Concrete; seventh. Wiley.
  • Meddah, S., Benkari, N., Al-Saadi, S. N. J., & Al Maktoumi’s, Y. (2020). Sarooj mortar: From a traditional building material to an engineered pozzolan -mechanical and thermal properties study-. Journal of Building Engineering, 32, 101754. https://doi.org/10.1016/j.jobe.2020.101754
  • Onescu, E.; Onescu, I.; Mosoarca, M.; Ion, A. Case study of the seismic vulnerability of a historical building in Timisoara, Romania. IOP Conference Series Materials Science and Engineering. 2022.
  • Peng, X., Zhuang, Z., & Yang, Q. (2022). Predictive modeling of compressive strength for concrete at super early age. Materials, 15(14), 4914. https://doi.org/10.3390/ma15144914
  • Shahmansouri, A. A., Yazdani, M., Hosseini, M., Akbarzadeh Bengar, H., & Farrokh Ghatte, H. (2022). The prediction analysis of compressive strength and electrical resistivity of environmentally friendly concrete incorporating natural zeolite using artificial neural network. Construction and Building Materials, 317, 125876. https://doi.org/10.1016/j.conbuildmat.2021.125876
  • Tiwari, K., Shrestha, H., & Guragain, R. (2020). Condition assessment and strengthening measured for historical building. International Journal of Innovative Science & Research Technology, 5(1), 522–534.
  • Topçu, İ. B., & Sarıdemir, M. (2007). Prediction of properties of waste aac aggregate concrete using artificial neural network. Computational Materials Science, 41(1), 117–125. https://doi.org/10.1016/j.commatsci.2007.03.010
  • Yang, D., Yan, C., Liu, S., Jia, Z., & Wang, C. (2022). Prediction of concrete compressive strength in saline soil environments. Materials, 15(13), 4663. https://doi.org/10.3390/ma15134663
  • Zheng, X., Peng, X., Zhao, J., & Wang, X. (2022). Trajectory prediction of marine moving target using deep neural networks with trajectory data. Applied Sciences, 12(23), 11905. https://doi.org/10.3390/app122311905
  • Zomarishidi, H. (2012). Wonderful Goharshad grand mosque and sacred architectural arts. Iranian Islamic City Studies.
  • Zucca, M., CrespiGiuseppe, P., Longarini, N., & Scamardo, A. (2020). the new foundation system of the Basilica Di collemaggio’s transept. International Journal of Masonry Research and Innovation, 5(1), 67–84. https://doi.org/10.1504/IJMRI.2020.104846
  • Zucca, M., Reccia, E., Longarini, N., & Cazzani, A. (2016). Seismic assessment and retrofitting of an historical masonry building damaged during the 2016 centro Italia seismic event. Applied Sciences, 12(22), 11786. https://doi.org/10.3390/app122211789