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Concepts for improving machine learning based landslide assessment
(Springer Netherlands, 2019)
The main idea of this chapter is to address some of the key issues that were recognized in Machine Learning (ML) based Landslide Assessment Modeling (LAM). Through the experience of the authors, elaborated in several case ...
A multi-fidelity wind surface pressure assessment via machine learning: A high-rise building case
(Elsevier, 2023)
Computational fluid dynamics (CFD) represents an attractive tool for estimating wind pressures and wind loads on high-rise buildings. The CFD analyses can be conducted either by low-fidelity simulations (RANS) or by ...
Estimation of ultimate strength of slender ccfst columns using artificial neural networks
(16th Congress of Association of Structural Engineers of Serbia, 2022)
This paper proposes the use of artificial neural network (ANN) algorithms to estimate the ultimate compressive strength of slender circular concrete-filled steel tubular (CCFST) columns. A dataset of 1051 samples was applied ...
Practical ANN prediction models for the axial capacity of square CFST columns
(Springer, 2023)
In this study, two machine-learning algorithms based on the artificial neural network
(ANN) model are proposed to estimate the ultimate compressive strength of square
concrete-filled steel tubular columns. The development ...
A Novel ANN Technique for Fast Prediction of Structural Behavior
(6th International Conference WE BUILD THE FUTURE 2022 International Construction Management Conference November 17-18, 2022, Belgrade, Serbia, 2022)
In recent decades, different concepts of machine learning (ML) have found applications in solving many engineering problems. Less time consumption in performing analyses, better optimization of the quality-price ratio and ...
Prediction of Ultimate Compressive Strength of CCFT Columns Using Machine Learning Algorithms
(8th International Conference Science and Practice, Kolasin, Montenegro, 2022)
The composite concrete-filled steel tube columns are structural members with numerous advantages over the traditional reinforced concrete or the pure steel members. The behavior of these columns is highly nonlinear. This ...
Implementation of Hybrid ANN-GWO Algorithm for Estimation of the Fundamental Period of RC-Frame Structures
(Second Serbian International Conference on Applied Artificial Intelligence, Kragujevac, Serbia, 2023)
The fundamental period (TFP) of vibration is one of the most important parameters in structural design since it is used to assess the dynamic response of the structures. It is the time taken by a structure or system to ...
A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat
(PeerJ, 2022)
A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal ...