Enhancing Wind Erosion Assessment of Metal Structures on Dry and Degraded Lands through Machine Learning

With the increasing construction activities in dry or degraded lands affected by wind-driven particle action, the deterioration of metal structures in such environments becomes a pressing concern. In the design and maintenance of outdoor metal structures, the emphasis has mainly been on preventing c...

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Bibliographic Details
Published in:Land (Basel) 2023-08, Vol.12 (8), p.1503
Main Authors: Terrados-Cristos, Marta, Ortega-Fernández, Francisco, Díaz-Piloñeta, Marina, Montequín, Vicente Rodríguez, Cabal, José Valeriano Álvarez
Format: Article
Language:English
Subjects:
Abrasion
Adaptive algorithms
Agricultural production
Artificial intelligence
Construction
Data collection
Datasets
degraded land
Environmental aspects
Experimental data
Impact tests
Laboratories
Land degradation
Learning algorithms
Machine learning
Maintenance
Measurement
metal structures
Prediction models
Sand & gravel
Soil conditions
Soil erosion
Soils
Sustainable development
Variables
Wind erosion
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Summary:With the increasing construction activities in dry or degraded lands affected by wind-driven particle action, the deterioration of metal structures in such environments becomes a pressing concern. In the design and maintenance of outdoor metal structures, the emphasis has mainly been on preventing corrosion, while giving less consideration to abrasion. However, the importance of abrasion, which is closely linked to the terrain, should not be underestimated. It holds significance in two key aspects: supporting the attainment of sustainable development goals and assisting in soil planning. This study aims to address this issue by developing a predictive model that assesses potential material loss in these terrains, utilizing a combination of the literature case studies and experimental data. The methodology involves a comprehensive literature analysis, data collection from direct impact tests, and the implementation of a machine learning algorithm using multivariate adaptive regression splines (MARS) as the predictive model. The experimental data are then validated and cross-verified, resulting in an accuracy rate of 98% with a relative error below 15%. This achievement serves two primary objectives: providing valuable insights for anticipating material loss in new structure designs based on prospective soil conditions and enabling effective maintenance of existing structures, ultimately promoting resilience and sustainability.
ISSN:2073-445X
2073-445X
DOI:10.3390/land12081503