Predicting Accident Outcomes in Cross-Border Pipeline Construction Projects Using Machine Learning Algorithms

Basit Öğe Kaydı
dc.authoridKoc, Kerim/0000-0002-6865-804X
dc.authoridTokdemir, Onur Behzat/0000-0002-4101-8560
dc.contributor.authorMammadov, Ahmad
dc.contributor.authorKazar, Gökhan
dc.contributor.authorKoç, Kerim
dc.contributor.authorTokdemir, Onur Behzat
dc.date.accessioned2024-09-11T19:50:44Z
dc.date.available2024-09-11T19:50:44Z
dc.date.issued2023
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractPipeline construction projects are necessary to provide gas and liquid energy transportation. Although various studies have investigated the contributing factors to accidents occurring in pipeline construction projects, there is a need for a predictive model for such incidents. The main purpose of this study is to provide an artificial intelligence-based model to predict the outcomes of occupational accidents. In this context, 1184 incident cases, including injury, near-miss, and asset-product damage, taken from a pipeline construction project constituted the primary dataset. Twelve prediction models are formed by changing the input domain according to the type of incident, time, and cause type attributes, leading to 12 distinct sub-datasets. Then, each dataset is tested with 11 different machine learning (ML) algorithms to derive an effective prescriptive model. The descriptive results show that low awareness of job hazards and improper vehicle operations were the most critical immediate causes, while failure in risk recognition and site supervision were the major root causes of pipeline construction accidents. Among the ML methods, the deep learning algorithm performed better than its counterparts in eight sub-datasets. Finally, a prescriptive model incorporating the ML application procedure is recommended for construction companies to reduce occupational accidents. Overall, the proposed model and findings are expected to contribute to preventing and reducing construction accidents in pipeline projects by adopting relevant strategies.en_US
dc.identifier.doi10.1007/s13369-023-07964-w
dc.identifier.endpage13789en_US
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85162013234en_US
dc.identifier.startpage13771en_US
dc.identifier.urihttps://doi.org/10.1007/s13369-023-07964-w
dc.identifier.urihttps://hdl.handle.net/11363/7654
dc.identifier.volume48en_US
dc.identifier.wosWOS:001007162700001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofArabian Journal For Science And Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectPipeline constructionen_US
dc.subjectConstruction accidentsen_US
dc.subjectRisk assessmenten_US
dc.subjectPrescriptive modelen_US
dc.subjectMachine learningen_US
dc.subjectOccupational health and safety (OHS)en_US
dc.titlePredicting Accident Outcomes in Cross-Border Pipeline Construction Projects Using Machine Learning Algorithmsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Makale / Article
Boyut:
1.13 MB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu