Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity
المؤلفون المشاركون
Golnaraghi, Sasan
Zangenehmadar, Zahra
Moselhi, Osama
Alkass, Sabah
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-01-02
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Productivity is described as the quantitative measure between the number of resources used and the output produced, generally referred to man-hours required to produce the final product in comparison to planned man-hours.
Productivity is a key element in determining the success and failure of any construction project.
Construction as a labour-driven industry is a major contributor to the gross domestic product of an economy and variations in labour productivity have a significant impact on the economy.
Attaining a holistic view of labour productivity is not an easy task because productivity is a function of manageable and unmanageable factors.
Compound irregularity is a significant issue in modeling construction labour productivity.
Artificial Neural Network (ANN) techniques that use supervised learning algorithms have proved to be more useful than statistical regression techniques considering factors like modeling ease and prediction accuracy.
In this study, the expected productivity considering environmental and operational variables was modeled.
Various ANN techniques were used including General Regression Neural Network (GRNN), Backpropagation Neural Network (BNN), Radial Base Function Neural Network (RBFNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) to compare their respective results in order to choose the best method for estimating expected productivity.
Results show that BNN outperforms other techniques for modeling construction labour productivity.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Golnaraghi, Sasan& Zangenehmadar, Zahra& Moselhi, Osama& Alkass, Sabah. 2019. Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity. Advances in Civil Engineering،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1116603
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Golnaraghi, Sasan…[et al.]. Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity. Advances in Civil Engineering No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1116603
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Golnaraghi, Sasan& Zangenehmadar, Zahra& Moselhi, Osama& Alkass, Sabah. Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity. Advances in Civil Engineering. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1116603
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1116603
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر