Abstract
Estimating the productivity of road construction equipment is a key factor in estimating
the cost of road construction projects successfully, and helps in determining the time
required to complete these projects. The objective of this study is to estimate the
productivity of road construction machines used in Iraq using fuzzy logic and neural
network, and monitoring the productivity of these equipment’s during the work period.
Most previous research has developed different mathematical models to estimate
equipment productivity, but they do not provide accurate or reliable production
estimation. Most of these mathematical models are based on ready-made mathematical
formula, regardless of the state of these machines and working conditions. They also
have difficulty describing the environment even if they are considered as a part of these
models. Estimating the productivity using these classical models depends on historical
records of performance, as well as personal opinions of the engineer and his special
judgments. In this research, the artificial neural network was used to estimate the
productivity of road construction equipment taking into account the various factors that
affect productivity. One of the most important factors affecting the productivity of road
construction equipment in Iraq is the weather, which depends on the location and date
of operation of the project; therefor, fuzzy expert system is used to estimate the weather
during the working period depending on the location and date of work. In addition, the
productivity estimation can be made continuously before and during the work, giving
a better view to the contractor to adjust his plans according to the project conditions
and follow-up work during entire work period
the cost of road construction projects successfully, and helps in determining the time
required to complete these projects. The objective of this study is to estimate the
productivity of road construction machines used in Iraq using fuzzy logic and neural
network, and monitoring the productivity of these equipment’s during the work period.
Most previous research has developed different mathematical models to estimate
equipment productivity, but they do not provide accurate or reliable production
estimation. Most of these mathematical models are based on ready-made mathematical
formula, regardless of the state of these machines and working conditions. They also
have difficulty describing the environment even if they are considered as a part of these
models. Estimating the productivity using these classical models depends on historical
records of performance, as well as personal opinions of the engineer and his special
judgments. In this research, the artificial neural network was used to estimate the
productivity of road construction equipment taking into account the various factors that
affect productivity. One of the most important factors affecting the productivity of road
construction equipment in Iraq is the weather, which depends on the location and date
of operation of the project; therefor, fuzzy expert system is used to estimate the weather
during the working period depending on the location and date of work. In addition, the
productivity estimation can be made continuously before and during the work, giving
a better view to the contractor to adjust his plans according to the project conditions
and follow-up work during entire work period
Keywords
equipment
Expert System
neural networks
Productivity
road construction