Artificial Neural Networks Applied in Civil Engineering
Source abstract
Abstract
In recent years, artificial neural networks (ANN) and artificial intelligence (AI), in general, have garnered significant attention with respect to their applications in several scientific fields, varying from big data management to medical diagnosis [...]
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Structural Analysis and Antiseismic Research, School of Civil Engineering, National Technical University of Athens, Heroon Polytechneiou 9, Zographou Campus, 157 80 Athens, Greece
Funding
Hellenic Foundation for Research and Innovation (H.F.R.I.)
References (25)
Show source-provided references
1. Lagaros, N.D., and Tsompanakis, Y. (2006). Intelligent Computational Paradigms in Earthquake Engineering, Idea Group Publishing.10.4018/978-1-59904-099-8 ↗
2. Structural reliability analysis of elastic-plastic structures using neural networks and Monte Carlo simulation10.1016/0045-7825(96)01011-0 ↗
3. Soft computing methodologies for structural optimization10.1016/S1568-4946(03)00040-1 ↗
4. Decker, R., and Lenz, H.J. (2007). VOS: A New Method for Visualizing Similarities Between Objects. Advances in Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization, Springer.
5. Mekaoui, N., and Saito, T. (2022). A Deep Learning-Based Integration Method for Hybrid Seismic Analysis of Building Structures: Numerical Validation. Appl. Sci., 12.10.3390/app12073266 ↗
6. Xiong, C., Zheng, J., Xu, L., Cen, C., Zheng, R., and Li, Y. (2021). Multiple-Input Convolutional Neural Network Model for Large-Scale Seismic Damage Assessment of Reinforced Concrete Frame Buildings. Appl. Sci., 11.10.3390/app11178258 ↗
7. Li, F., Song, Y., and Shan, Y. (2020). Joint Extraction of Multiple Relations and Entities from Building Code Clauses. Appl. Sci., 10.10.3390/app10207103 ↗
8. Accelerated topology optimization by means of deep learning
9. Kallioras, N., Nordas, A., and Lagaros, N. (2021). Deep Learning-Based Accuracy Upgrade of Reduced Order Models in Topology Optimization. Appl. Sci., 11.10.3390/app112412005 ↗
10. Roberts, R., Inzerillo, L., and Di Mino, G. (2021). Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions. Appl. Sci., 11.10.3390/app11062458 ↗
11. Makki, A., and Mosly, I. (2021). Predicting the Safety Climate in Construction Sites of Saudi Arabia: A Bootstrapped Multiple Ordinal Logistic Regression Modeling Approach. Appl. Sci., 11.10.3390/app11041474 ↗
12. Wu, N. (2021). Predicting the Compressive Strength of Concrete Using an RBF-ANN Model. Appl. Sci., 11.10.3390/app11146382 ↗
13. Ahmad, A., Lagaros, N., and Cotsovos, D. (2021). Neural Network-Based Prediction: The Case of Reinforced Concrete Members under Simple and Complex Loading. Appl. Sci., 11.10.3390/app11114975 ↗
14. Rahman, S., and Al-Ameri, R. (2021). Experimental Investigation and Artificial Neural Network Based Prediction of Bond Strength in Self-Compacting Geopolymer Concrete Reinforced with Basalt FRP Bars. Appl. Sci., 11.10.3390/app11114889 ↗
15. Mahamat, A., Boukar, M., Ibrahim, N., Stanislas, T., Linda Bih, N., Obianyo, I., and Savastano, H. (2021). Machine Learning Approaches for Prediction of the Compressive Strength of Alkali Activated Termite Mound Soil. Appl. Sci., 11.10.3390/app11114754 ↗
16. Lin, C., and Wu, N. (2021). An ANN Model for Predicting the Compressive Strength of Concrete. Appl. Sci., 11.10.3390/app11093798 ↗
17. Kashif, M., Naseem, A., Iqbal, N., Winne, P., and De Backer, H. (2021). Numerical Evaluation of Early-Age Crack Induction in Continuously Reinforced Concrete Pavement with Different Saw-Cut Dimensions Subjected to External Varying Temperature Field. Appl. Sci., 11.10.3390/app11041659 ↗
18. Kim, J., Kim, A., and Lee, S. (2020). Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures. Appl. Sci., 10.10.3390/app10228105 ↗
19. Ding, Y., Zhang, J., Jia, Y., Chen, X., Wang, X., and Meng, F. (2020). Study on Two-Phase Fluid-Solid Coupling Characteristics in Saturated Zone of Subgrade Considering the Effects of Fine Particles Migration. Appl. Sci., 10.10.20944/preprints202009.0527.v1 ↗
20. Azmoon, B., Biniyaz, A., and Liu, Z. (2021). Evaluation of Deep Learning against Conventional Limit Equilibrium Methods for Slope Stability Analysis. Appl. Sci., 11.10.3390/app11136060 ↗
21. Oh, D., Kong, S., Lee, Y., and Park, H. (2021). Prediction of Change Rate of Settlement for Piled Raft Due to Adjacent Tunneling Using Machine Learning. Appl. Sci., 11.10.3390/app11136009 ↗
22. Reliability Analysis of Geostructures based on Metaheuristic Optimization, Appl10.1016/j.asoc.2014.04.026 ↗
23. Mishra, P., Samui, P., and Mahmoudi, E. (2021). Probabilistic Design of Retaining Wall Using Machine Learning Methods. Appl. Sci., 11.10.3390/app11125411 ↗
24. Mitu, S., Rahman, N., Nayan, K., Zulkifley, M., and Rosyidi, S. (2021). Implementation of Machine Learning Algorithms in Spectral Analysis of Surface Waves (SASW) Inversion. Appl. Sci., 11.10.3390/app11062557 ↗
25. Saldaña, M., González, J., Pérez-Rey, I., Jeldres, M., and Toro, N. (2020). Applying Statistical Analysis and Machine Learning for Modeling the UCS from P-Wave Velocity, Density and Porosity on Dry Travertine. Appl. Sci., 10.10.3390/app10134565 ↗