Development of advanced planning and estimating model for scaffolding manhour

Heavy industrial construction projects consist of oil refinaries which generally involve complex structures, large-scale sites, and large numbers of workers from different disciplines such as civil, mechanical, and chemical. These disciplines may require similar or completely different scaffolding systems in order for workers to not only access their working areas but also move material horizontally and vertically. The variability of scaffolding systems required by the different disciplines operating on these complex sites can be a primary cause of increased project costs and project time delay. Due to this variability, construction company is challenging to estimate and plan the scaffolding time and cost accurately and efficiently. In practice, scaffolding is estimated and planed by 60% of the total project cost or approximately 30–40% of the total man-hours of construction works in the project. This approximate planning and estimating method leads to be difficulty to complete the construction projects on-time within the budget. To eliminate current scaffolding estimating and planning practice, this research proposes to develop a scaffolding manhour predictive model using an artificial neural network (ANN) algorithm which is a deep-learning algorithm.

Faculty Supervisor:

Sang Han


Wenjing Chu


PCL Industrial Management Inc


Engineering - civil


Construction and infrastructure


Concordia University



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