Probabilistic Duration Estimation Model for High-Rise Structural Work

Abstract

The duration of a construction project is a key factor to consider before starting a new project, as it can determine project success or failure. Despite the high level of uncertainty and risk involved in construction, current construction planning relies on traditional deterministic scheduling methods that cannot clearly ascertain the level of uncertainty involved in a project. This, subsequently, can prolong a project's duration, particularly when that project is high-rise structural work, which is not yet a common project type in Korea. Indeed, among construction processes, structural work is notable, as it is basically performed outdoors. Thus, no matter how precisely a schedule is developed, such projects can easily fail due to unexpected events that are beyond the planner's control, such as changes in weather conditions. Therefore, in this study, to cope with the uncertainties involved in high-rise building projects, a probabilistic duration estimation model is developed in which both weather conditions and work cycle time for unit work are considered to predict structural work duration. According to the proposed estimation model, weather variables are divided into two types: weather conditions that result in nonworking days and weather conditions that result in work productivity rate ͑WPR͒ change. Obtained from actual previous data, the WPR is used with relevant nonworking day weather conditions to modify the actual number of working days per calendar days. Furthermore, on the basis of previous research results, the cycle time of the unit work area is assumed to follow the ␤ probability distribution function. Thus, the probabilistic duration model is valid for 95% probability. Finally, a case study is conducted that confirms the model can be practically used to estimate more reliable and applicable probabilistic durations of structural work. Indeed, this model can assist schedulers and site workers by alerting them, at the beginning of a project, to project uncertainties that specifically pertain to structural work and the weather. Thus, the proposed model can enable personnel to easily amend, and increase the reliability of, the construction schedule at hand.

Formula for Determining the Construction Workers Productivity Including Environmental Factors

“…Weather conditions have a huge impact on construction activities. Many studies have modelled the influence of temperature and air humidity, as well as that of extreme weather conditions, on the productivity of construction workers [25,26].…”

“…To construct a model to measure the productivity of construction workers, on the basis of an analysis of the literature, 17 factors were selected and categorized into five groups (Table 1). These factors are most often cited in the literature [5,16,22,23,25,26,28] as having a fundamental impact on the productivity of construction workers. They are also characterized by universality, i.e., they have an impact in all types of construction work and are not characteristic only for a selected group of works.…”

Construction of ropeways in Slovakia in terms of pre-construction design using helicopters (case study)

“…These authors have successfully used the simulation approach (e.g. Lee et al [11], Jung et al, [12]) to assess the significance of this impact. However, they simulated only "conventional" construction processes using conventional cranes and other conventional construction machinery [13][14][15].…”

Modelling the Stochastic Dependence Underlying Construction Cost and Duration

“…The fitness of several general distribution types such as Weibull (Nassar et al 2005) and uniform (Fine, Hackemer 1970;Grinyer, Whittaker 1973;Van Cauwelaert, Heynig 1979), Gamma (Friedman 1956), and lognormal (Skitmore 1991) have been explored for construction cost or duration. Other studies investigate the distribution of duration under different weather conditions (Lee et al 2009), project and contract types (Irfan et al 2011), and logic types (Wang 2005). Based on predefined distributions, several probabilistic methods such as Monte Carlo simulation (MCS), the program evaluation and review technique (PERT) and agent-based simulation have been widely used to address the uncertainty in construction duration (Ahuja, Nandakumar 1985;Ballesteros-Pérez 2017;Erol et al 2017;Farshchian et al 2017;Karabulut 2017;Moret, Einstein 2016).…”