Using Multivariable Linear Regression Technique for Modeling Productivity Construction in Iraq

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Open Journal of Civil Engineering

ISSN Print: 2164-3164
ISSN Online: 2164-3172
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"Using Multivariable Linear Regression Technique for Modeling Productivity Construction in Iraq"
written by Faiq Mohammed Sarhan Al-Zwainy, Mohammed Hashim Abdulmajeed, Hadi Salih Mijwel Aljumaily,
published by Open Journal of Civil Engineering, Vol.3 No.3, 2013

has been cited by the following article(s):

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[1]	Construction productivity prediction through Bayesian networks for building projects: Case from Vietnam
	Engineering, Construction and Architectural …, 2022

[2]	Development and comparative of a new meta-ensemble machine learning model in predicting construction labor productivity
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[3]	The Impact of Buildability Factors on Formwork in Residential Building Construction
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[4]	Concrete Pouring Production Rate Estimation: A Bayesian Network Approach
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[7]	Predicting Maintenance and Rehabilitation Cost for Buildings Based on Artificial Neural Network and Fuzzy Logic
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[8]	Dynamic Feature Selection for Accurately Predicting Construction Productivity Using Symbiotic Organisms Search-Optimized Least Square Support Vector Machine
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[9]	Evaluating Implementation of Electric Power Generation Projects in Iraq
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[10]	Assessment of feature selection for student academic performance through machine learning classification
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[11]	Bond the Gap between Academic and Industry Requirements for Undergraduate Civil Engineering Students in Iraq
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[16]	Modelling manpower and equipment productivity in tall residential building projects in developing countries
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[17]	Construction productivity fuzzy knowledge base management system
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[18]	Predicción de la resistencia térmica de las incrustaciones en los enfriadores de ácido sulfhídrico
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[19]	The Application of Cobb-Douglas Function in Forecasting the Duration of Internet Public Opinions Caused by the Failure of Public Policies
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[20]	Diagnosing the Causes of Failure in the Construction Sector Using Root Cause Analysis Technique
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[22]	Asset Condition Assessment Model for Healthcare Facilities
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[23]	Forecasting the Cost of Structure of Infrastructure Projects Utilizing Artificial Neural Network Model (Highway Projects as Case Study)
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[24]	Factors affecting construction labour productivity in Iraq using Basra City as a case study
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[25]	Optimising cost and availability estimates at the bidding stage of performance-based contracting
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[26]	Merjenje in določanje produktivnosti proizvodnih podjetij: primer 20 MN stiskalnice aluminija v Impol PCP doo
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[27]	Diagnose the Causes of Cost Deviation in Highway Construction Projects by Using Root Cause Analysis Techniques
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[28]	Modeling Manpower and Equipment Productivity in Tall Building Construction Projects
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[29]	Perception Variance of TACT Productivity between General Contractor and Subcontractors
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[31]	Building a Mathematical Model for Predicting the Cost of the Communication Towers Projects Using Multifactor Linear Regression Technique
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[32]	Toward a Sustainable World Institutional Buildings
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[33]	Estimating Productivity of Brickwork item using Logistic and Multiple Regression Approaches
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[34]	Application Intelligent Predicting Technologies in Construction Productivity
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[35]	Labour Productivity Analysis Using Multi-variable Linear Regression
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[36]	Development Mathematical Model for Brick Works Productivity by Using Support Vector Machine
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[37]	Development of Rainfall Empirical Models for Osun Watershed, Nigeria.
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[38]	Institutional Factors Affecting A Transitional City: An Integrated Perspective On Housing Market Development In Yangon, Myanmar
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[41]	Predicting productivity in construction industry utilizing multiple linear regression technique and artificial neural network technique: A review for research and …
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[42]	A Review Paper on Prediction of Construction Productivity Using Artificial Neural Network Model

[1]	Construction productivity prediction through Bayesian networks for building projects: case from Vietnam Engineering, Construction and Architectural Management, 2023 DOI:10.1108/ECAM-07-2021-0602

[2]	Machine learning for construction crew productivity prediction using daily work reports Automation in Construction, 2023 DOI:10.1016/j.autcon.2023.104891

[3]	Rendimiento de mano de obra en excavaciones a mano mediante regresión lineal. Caso de estudio: ciudad de Cuenca Ciencia Digital, 2023 DOI:10.33262/cienciadigital.v7i3.2629

[4]	Machine learning for construction crew productivity prediction using daily work reports Automation in Construction, 2023 DOI:10.1016/j.autcon.2023.104891

[5]	Improvement of Productivity in Buildings Construction Selected Scientific Papers - Journal of Civil Engineering, 2023 DOI:10.2478/sspjce-2023-0005

[6]	Development and comparative of a new meta-ensemble machine learning model in predicting construction labor productivity Engineering, Construction and Architectural Management, 2022 DOI:10.1108/ECAM-08-2021-0692

[7]	Construction productivity prediction through Bayesian networks for building projects: case from Vietnam Engineering, Construction and Architectural Management, 2022 DOI:10.1108/ECAM-07-2021-0602

[8]	Modelling labour productivity using SVM and RF: a comparative study on classifiers performance International Journal of Construction Management, 2022 DOI:10.1080/15623599.2020.1744799

[9]	Trends in Civil Engineering and Challenges for Sustainability Lecture Notes in Civil Engineering, 2021 DOI:10.1007/978-981-15-6828-2_8

[10]	Dynamic feature selection for accurately predicting construction productivity using symbiotic organisms search-optimized least square support vector machine Journal of Building Engineering, 2021 DOI:10.1016/j.jobe.2020.101973

[11]	Dynamic feature selection for accurately predicting construction productivity using symbiotic organisms search-optimized least square support vector machine Journal of Building Engineering, 2020 DOI:10.1016/j.jobe.2020.101973

[12]	Concrete Pouring Production Rate Estimation: A Bayesian Network Approach Construction Research Congress 2020, 2020 DOI:10.1061/9780784482889.063

[13]	Predicting Maintenance and Rehabilitation Cost for Buildings Based on Artificial Neural Network and Fuzzy Logic International Journal of Computational Intelligence and Applications, 2020 DOI:10.1142/S1469026820500017

[14]	Evaluating Implementation of Electric Power Generation Projects in Iraq IOP Conference Series: Materials Science and Engineering, 2020 DOI:10.1088/1757-899X/901/1/012034

[15]	Assessment of feature selection for student academic performance through machine learning classification Journal of Statistics and Management Systems, 2019 DOI:10.1080/09720510.2019.1609729

[16]	Bond the Gap between Academic and Industry Requirements for Undergraduate Civil Engineering Students in Iraq IOP Conference Series: Materials Science and Engineering, 2019 DOI:10.1088/1757-899X/584/1/012001

[17]	Determinant factors affecting farmers’ income of rice farming in Indonesia IOP Conference Series: Earth and Environmental Science, 2019 DOI:10.1088/1755-1315/343/1/012115

[18]	Construction productivity fuzzy knowledge base management system Canadian Journal of Civil Engineering, 2018 DOI:10.1139/cjce-2017-0540

[19]	The Application of Cobb-Douglas Function in Forecasting the Duration of Internet Public Opinions Caused by the Failure of Public Policies Journal of Systems Science and Systems Engineering, 2018 DOI:10.1007/s11518-018-5384-6

[20]	Diagnosing the Causes of Failure in the Construction Sector Using Root Cause Analysis Technique Journal of Engineering, 2018 DOI:10.1155/2018/1804053

[21]	Forecasting the Cost of Structure of Infrastructure Projects Utilizing Artificial Neural Network Model (Highway Projects as Case Study) Indian Journal of Science and Technology, 2017 DOI:10.17485/ijst/2017/v10i19/108567

[22]	Forecasting the Cost of Structure of Infrastructure Projects Utilizing Artificial Neural Network Model (Highway Projects as Case Study) Indian Journal of Science and Technology, 2017 DOI:10.17485/ijst/2017/v10i20/108567

[23]	Modeling Manpower and Equipment Productivity in Tall Building Construction Projects Journal of The Institution of Engineers (India): Series A, 2017 DOI:10.1007/s40030-017-0256-5

[24]	Diagnose the Causes of Cost Deviation in Highway Construction Projects by Using Root Cause Analysis Techniques Arabian Journal for Science and Engineering, 2017 DOI:10.1007/s13369-017-2850-2

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	1655362766

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