Figure 2.1: Straight line Model best fitting curve
The straight line model has been shown to be the most reliable predictor for the effect of learning on construction activities (Everett & Farghal, 1994).
Example
Assume the surfacing of a wearing course layer in a road construction project has an initial duration of 10 days. It is repeated 10 consecutive times without any interruptions using only one crew. This activity has a learning rate of 90%. From Equation 2.1:
Y2 = 10 x 2 (log 0.9 / log 2) = 9.5
So, the duration at unit 2 = n x Yn – Yn-1 = 2x9.5 – 10 = 9 days
From this equation, Table 2.1compares LOB duration (ES & EF) before and after applying the learning effect.
Table 2.1: LOB schedule before and after applying the learning effect
Learning effect (a) LOB
(no learning) (b)
Unit Y Dur. Cum. Time ES EF ES EF
1 10 10 10 0.0 10.0 0 10
2 9.0 8.0 18.0 10.0 18.0 10 …show more content…
ALISS is designed to have a well-structured system having both a web-based and standalone versions. It is programmed using several languages such as visual basic and visual basic script. It also uses SQL server and Microsoft access for storing databases. This program is divided into five interconnected modules.
The first module is the input module where the user inputs all needed information for running the program, starting from the project data to the activities data. Activities are divided into three categories: linear, non-linear and discrete activities. The user also identifies the learning rates of repetitive activities to be included in the model. Later, these data are used to calculate the early and late dates for each activity and draw the LOB diagram for this project. To apply the effect of learning, the program applies the approach presented by Arditi et al