Construction Cost Estimation and Employee Tracking Management System
Keywords:
Global System for mobile, Back-Propagation, Neural Network, Cost Estimation, Material Prediction, Global Positioning System, FirebaseAbstract
The aim of this proposed system is to highlight the importance of
material management as materials make considerable percentage
of total construction costs and also employee tracking. Construction
material and equipment constitute more than 70% of the total cost for
a construction project. Considering different employee management
needs, our systems come with remote access features for builders,
which will allow you to manage your workforce anytime. As Android
applications are increasingly gaining popularity these days, we are
developing an Android based application for location tracking and
conferencing which can be used by employees working on site, outside
office. One of the main features in employee management system is
distance tracking for employees and report generation for particular site.
This effective location tracking mechanism saves both time and money
of construction industry. As adequate construction cost estimation
is main factor in any type of construction project and also material
management is beneficial towards the material savings, combining this
both facts together, we are developing Web Application for prediction
of material requirement and cost. For cost estimation and prediction
we are using neural network algorithm to predict accurate cost and also
two techniques of neural network namely feed-forward neural network
architecture and back-propagation learning technique are included in
this system. For secure data transfer we are using AES algorithm.
References
Du Z, Binyo-ng Li. Construction Project Cost Estimation
Based on Improved BP Neural Network. IEEE 2017; 3(2).
Sphurti S, Nagaraj V. Dharwadk-ar. Cost Estimation of
Civil Construction Projects using Machine Learning
Paradigm. IEEE I-SMAC 2017.
Dhotre P, Jha M, Rasal G et al. Employee Monitoring
and Management System using GPS and Android”
International Research Journal of Engineering and
Technology (IRJET) 2017.
Magdum SK, Adamuthe AC. Construction Cost
Prediction Using Neutral Networks. ICTACT Journal
on soft computing 2017; 8(1).
Yadav R, Vyas M, Vyas V et al. Cost Estimation Model
for Residential Building using Artificial Neural Network.
International Journal of Engineering Research and
Technology 2016; 5(2): 312-314.
Kim GH, Shin JM, Kim S. Comparison of School
Building Construction Costs Estimation Methods Using
Regression Analysis, Neural Network and Support
Vector Machine. Journal of Building Construction and
Planning Research 2013.
Patel K, Vyas C. Construction materials management
on project sites. National Conference on Recent Trends
in Engineering Technology. 2011.
Cheng MY, Tsai HC, Sudjono E. Conceptual Cost Estimates
using Evolutionary Fuzzy Hybrid Neural Network for
Projects in Construction Industry. International Journal
on Expert Systems with Application 2010; 37(6): 4224-
Luu VT, Kim SY. Neural Network Model for Construction
Cost Prediction of Apartment Projects in Vietnam.
Korean Journal of Construction Engineering and Management 2009; 10(3): 139-147
