NEURAL NETWORKS FOR ESTIMATING THE CERAMIC PRODUCTIVITY OF WALLS

محتوى المقالة الرئيسي

Sawsan Rasheed Mohammed
Ali Sabri Tofan

الملخص

Productivity estimating of a construction operation is an essential tool for the successful completion of the construction process. Productivity of a construction operation is defined as output of the system per unit of time. In this research Artificial Neural Networks approaches are presented. The main reason for using neural nerworks for construction productivity estimation is the requirement of performing complex mapping of environment and management factors to productivity. A generic description of the artificial neural networks model is provided, followed by summarized factors that affect ceramic labor productivity, then neural-network model are developed for Estimating ceramic walls productivity, the input data for the model based on experienced superintendents employed by a leading construction general contractor, test results show that the ANN approach can produce a sufficiently accurate estimate with a limited data-collection effort, and thus has the potential to provide an efficient tool for construction productivity estimation.

تفاصيل المقالة

كيفية الاقتباس
"NEURAL NETWORKS FOR ESTIMATING THE CERAMIC PRODUCTIVITY OF WALLS" (2011) مجلة الهندسة, 17(02), ص 200–217. doi:10.31026/j.eng.2011.02.02.
القسم
Articles

كيفية الاقتباس

"NEURAL NETWORKS FOR ESTIMATING THE CERAMIC PRODUCTIVITY OF WALLS" (2011) مجلة الهندسة, 17(02), ص 200–217. doi:10.31026/j.eng.2011.02.02.

تواريخ المنشور

المراجع

AACE International Recommended

Practice No. 25R-03

Estimating Lost

Labor Productivity in Construction

Claims", TCM Framework: 6.4

Forensic Performance Assessment / 2004,

Sign in

-

Ali Sabri Tofan "Neural Networks for

Estimating the Productivity Ceramic of

Walls and Floors" MSc. Thesis, College

of Engineering, Al-Baghdad University

/2009

Bain, D. "The Productivity Prescription",

McGraw Hill, New York, 1982.

Burnham, D.C. "Productivity; an

Overview". Handbook of Industrial

Engineering, John Wiley & Sons, 1982.

Construction Industry Institute (CII), "An

Analysis of the Methods for Measuring

Construction Productivity", SD-13,

Austin, Texas, 1984

Feldman, D.C. and Arnol, H.J. "Managing Group and Individual Behavior in Organization", McGraw Hill; 1983.

• Kavanaugh, Thomas C., Frank Muller & James J. O'Brien, "Construction Management: A Professional Approach", McGraw-Hill Company, New York, 1978. Book

• Mukherjee, S.K. and Sing, D. "Towards High Productivity", Report of a Seminar on Higher Productivity in Public Sector Production Enterprises, New Delhi Bureau of Public Enterprises, 1975.

Gould, F. Managing "The Construction Process: Estimating, Scheduling, and Project Control" 2nd Ed. Prentice-Hall. Upper Saddle River. N. d. Halligan. D.W. Demset2. L.A.

Brown. J.d and Pacc, C.B 2002. Lefton, RE. and Fellows "Effective Motivation Through Perf Performance Appraisal", John Wiley & Sons, 1982.

. Prokopenko, J. "Productivity Management", a Practical Handbook, International Labor Office, Geneva, 1987.

TRB, "Use of Artificial Neural Networks in Geotechnical and Pavement Systems", Transportation Research Circular No. e- c012, (1999).

Shahin, M.A. "Use of Artificial Neural Networks for Predicting Settlement of Shallow Foundations on Cohesionless Soils", Ph. D. Thesis, Department of Civil and Environmental Engineering, University of Adelaide, 2003.

Roman M. Balabin, Ekaterina 1. Lomakina. "Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies", 2009. Find text or tools Q C