Predicting Construction Crew Productivity for Concrete-Pouring Operations-Reference-Cited by-同舟云学术

Predicting Construction Crew Productivity for Concrete-Pouring Operations

Published:2024-05 Issue:2 Volume:16 Page:
ISSN:1943-4162
Container-title:Journal of Legal Affairs and Dispute Resolution in Engineering and Construction
language:en
Short-container-title:J. Leg. Aff. Dispute Resolut. Eng. Constr.

Author:

Patel Parth¹,Patel D. V.²,Lad V. H.³,Patel K. A.⁴,Patel D. A.⁵

Affiliation:

1. Formerly, Postgraduate Student, Dept. of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007, India.

2. Postgraduate Student, Dept. of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007, India.

3. Assistant Professor, Civil Engineering Dept., Institute of Technology, Nirma Univ., Ahmedabad, Gujarat 382481, India.

4. Assistant Professor, Dept. of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007, India (corresponding author).

5. Associate Professor, Dept. of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007, India.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Law,Engineering (miscellaneous),Safety, Risk, Reliability and Quality,Civil and Structural Engineering,Building and Construction

Link

https://ascelibrary.org/doi/pdf/10.1061/JLADAH.LADR-1034

Reference40 articles.

1. Factors affecting construction labour productivity in Yemen

2. Application intelligent predicting technologies in construction productivity;Al-Zwainy F. M.;Am. J. Eng. Technol. Manage.,2016

3. Productivity estimation model for bricklayer in construction projects using neural network;Aswed G. K.;Al-Qadisiyah J. Eng. Sci.,2016

4. Bokor O. L. Florez-Perez G. Pesce and N. Gerami Seresht. 2021. “Using artificial neural networks to model bricklaying productivity.” In Proc. 2021 European Conf. on Computing in Construction 52–58. Dublin Ireland: Univ. College Dublin.

5. The use of artificial neural networks in construction management: a review