[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117313-en":3,"doc-seo-117313-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},117313,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Enhancing Wind Erosion Assessment of Metal Structures on Dry and Degraded Lands through Machine Learning","Construction in dry or degraded lands increasingly faces deterioration of metal structures from wind-driven particle abrasion. Outdoor design and maintenance have focused mainly on preventing corrosion, leaving abrasion’s terrain-linked role insufficiently addressed for sustainable development goals and soil planning. This study develops a predictive model for potential material loss using literature case studies and experimental impact-test data. A machine learning approach with multivariate adaptive regression splines (MARS) is validated through cross-verification, achieving about 98% accuracy and under 15% relative error to support design decisions and maintenance for resilience.","land  \nArticle  \nEnhancing Wind Erosion Assessment of Metal Structures on Dry and Degraded Lands through Machine Learning  \nMarta Terrados-Cristos *, Francisco Ortega-Fern¡ndez, Marina D½az-Piloñeta, Vicente Rodr½guez Montequ½n and Jos² Valeriano 􀂁lvarez Cabal  \nCitation: Terrados-Cristos, M.; Ortega-Fernández, F.; Díaz-Piloñeta, M.; Montequín, V.R.; Cabal, J.V.Á .  \nEnhancing Wind Erosion Assessment of Metal Structures on Dry and Degraded Lands through Machine Learning. Land 2023, 12, 1503 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)land12081503  \nAcademic Editor: Chuanrong Zhang  \nReceived: 16 June 2023  \nRevised: 22 July 2023  \nAccepted: 26 July 2023  \nPublished: 28 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nProject Engineering Department, University of Oviedo, 33004 Oviedo, Spain  \n* Correspondence: marta.terrados@api.uniovi.es  \nAbstract: With the increasing construction activities in dry or degraded lands affected by winddriven 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 significancein 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.  \nKeywords: wind erosion; degraded land; metal structures; abrasion; machine learning  \n1. Introduction  \nWind erosion is a natural process that involves removal, transport and deposition of coarse and fine particles, primarily sand, by the wind [1] . Differences in atmospheric pressure generate air movements capable of eroding surface materials (also known as abrasion) when velocities reach sufficient levels [2] . The scientific community has increasingly recognized the significance of wind erosion due to its impact on soil health, agricultural production, climate and structures resilience [3] . Efforts have been devoted to simulating and predicting wind-driven effects, including soil erosion, to control land degradation and implement appropriate agricultural management practices [4] . Various methods, ranging from empirical equations for average soil erosion [5,6] to advanced models predicting crop yields and conservation of natural resources [7–9], have been developed.  \nHowever, wind erosion is gaining increasing relevance in other fields that have not been extensively studied. The durability of metal structures is greatly influenced by damage caused by wind erosion, particularly in degraded areas where wind-driven particle movement is more intense [10] . While the degradation of metal structures in outdoor co","cbCaim77r0CL5zYo","https://ap.wps.com/l/cbCaim77r0CL5zYo","pdf",4193611,1,16,"English","en",105,"# Introduction\n## Wind erosion background and relevance\n## Impact on metal structures and abrasion vs corrosion\n## Soil suitability and implications for construction","[{\"question\":\"Why is abrasion an important concern for metal structures in dry or degraded lands?\",\"answer\":\"Wind-driven particles cause abrasion, and its effects are closely linked to terrain. Reducing abrasion risk supports sustainable development goals and more effective land planning.\"},{\"question\":\"What does the study aim to achieve?\",\"answer\":\"It builds a predictive model to estimate potential material loss in dry and degraded terrains using literature case studies and experimental impact-test data.\"},{\"question\":\"How is the predictive model implemented and validated?\",\"answer\":\"The method combines data collection with a machine learning algorithm using multivariate adaptive regression splines (MARS). Experimental results are validated through cross-verification, reaching about 98% accuracy with relative error below 15%.\"}]",1785675121,40,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"enhancing-wind-erosion-assessment-of-metal-structures-on-dry-and-degraded-lands-through-machine-learning","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/enhancing-wind-erosion-assessment-of-metal-structures-on-dry-and-degraded-lands-through-machine-learning/117313/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is abrasion an important concern for metal structures in dry or degraded lands?","Question",{"text":74,"@type":75},"Wind-driven particles cause abrasion, and its effects are closely linked to terrain. Reducing abrasion risk supports sustainable development goals and more effective land planning.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What does the study aim to achieve?",{"text":79,"@type":75},"It builds a predictive model to estimate potential material loss in dry and degraded terrains using literature case studies and experimental impact-test data.",{"name":81,"@type":72,"acceptedAnswer":82},"How is the predictive model implemented and validated?",{"text":83,"@type":75},"The method combines data collection with a machine learning algorithm using multivariate adaptive regression splines (MARS). Experimental results are validated through cross-verification, reaching about 98% accuracy with relative error below 15%.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":28,"slug":117},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]