Modelling labour productivity using SVM and RF: a comparative study on classifiers performance

The purpose of this paper is to propose a data-driven approach for preparation of Construction Labour Productivity (CLP) models from influencing labour factors. Two state-of-art machine learning-ba...

[1]  Yong Pan,et al.  Advantages of support vector machine in QSPR studies for predicting auto-ignition temperatures of organic compounds , 2008 .

[2]  Chester G. Wilmot,et al.  Neural Network Modeling of Highway Construction Costs , 2005 .

[3]  Zohar J. Herbsman,et al.  Research of factors influencing construction productivity , 1990 .

[4]  Aminah Robinson Fayek,et al.  System model for analysing construction labour productivity , 2016 .

[5]  K Shashank,et al.  Analysis of Key Factors Affecting the Variation of Labour Productivity in Construction Projects , 2014 .

[6]  Shamsuddin Shahid,et al.  Model output statistics downscaling using support vector machine for the projection of spatial and temporal changes in rainfall of Bangladesh , 2018, Atmospheric Research.

[7]  Vicente González,et al.  A Production Model for Construction: A Theoretical Framework , 2015 .

[8]  Serdar Ulubeyli,et al.  A different approach to construction labour in Turkey: comparative productivity analysis , 2004 .

[9]  Onur Behzat Tokdemir,et al.  Comparison of Case-Based Reasoning and Artificial Neural Networks , 1999 .

[10]  Suman Rani.  Study of Impementation of Total Quality Management for Defect Reduction in Manufacturing Industry , 2012 .

[11]  Hyunsoo Kim,et al.  Productivity Forecasting of Newly Added Workers Based on Time-Series Analysis and Site Learning , 2015 .

[12]  Faiq Mohammed Sarhan Al-Zwainy,et al.  Using Multivariable Linear Regression Technique for Modeling Productivity Construction in Iraq , 2013 .

[13]  Chang-Wook Lee,et al.  Groundwater Productivity Potential Mapping Using Logistic Regression and Boosted Tree Models: The Case of Okcheon City in Korea , 2018, Advances in Remote Sensing and Geo Informatics Applications.

[14]  Zain Al-Kofahi.  Dynamic Modeling Approach to Quantify Change Orders Impact on Labor Productivity , 2016 .

[15]  André Stumpf,et al.  Combining Random Forests and object-oriented analysis for landslide mapping from very high resolution imagery , 2011 .

[16]  Nakju Lett Doh,et al.  Development of conceptual model of construction factory for automated construction , 2009 .

[17]  Tao Zhang,et al.  Determination of ocean primary productivity using support vector machines , 2008 .

[18]  Ercan Erdis,et al.  Modelling masonry crew productivity using two artificial neural network techniques , 2014 .

[19]  Hemanta Doloi.  Twinning Motivation, Productivity and Management Strategy in Construction Projects , 2007 .

[20]  Paulino Teixeira,et al.  Productivity, wages, and the returns to firm-provided training: fair shared capitalism? , 2013 .

[21]  David Wheeler,et al.  International investment location decisions: The case of U.S. firms , 1992 .

[22]  M. Maliranta,et al.  The Roles of Job and Worker Restructuring in Aggregate Wage Growth Dynamics , 2019 .

[23]  David W. Halligan,et al.  Action-Response Model and Loss of Productivity in Construction , 1994 .

[24]  Abhijit Ghosh,et al.  Modeling blast furnace productivity using support vector machines , 2011 .

[25]  Osama Moselhi,et al.  Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity , 2019, Advances in Civil Engineering.

[26]  Jahidul Alum,et al.  Construction productivity: Issues encountered by contractors in Singapore , 1995 .

[27]  Abdullah M. Alsugair,et al.  Artificial neural network approach for pavement maintenance , 1998 .

[28]  Dong Zhai,et al.  Relationship between Automation and Integration of Construction Information Systems and Labor Productivity , 2009 .

[29]  Dong Zhai,et al.  Relationship between Changes in Material Technology and Construction Productivity , 2009 .

[30]  David Card.  The Causal Effect of Education on Learning , 1999 .

[31]  H. Randolph Thomas,et al.  Factor Model of Construction Productivity , 1987 .

[32]  R. Murugasan,et al.  Modelling manpower and equipment productivity in tall residential building projects in developing countries , 2018 .

[33]  William F. Maloney,et al.  Valence of and Satisfaction with Job Outcomes , 1985 .

[34]  A. Samer Ezeldin,et al.  Neural Networks for Estimating the Productivity of Concreting Activities , 2006 .

[35]  Awad S. Hanna,et al.  Impact of extended overtime on construction labor productivity , 2005 .

[36]  Simaan M. AbouRizk,et al.  NEURAL NETWORK MODEL FOR ESTIMATING CONSTRUCTION PRODUCTIVITY , 1997 .

[37]  Daniel A. Vila,et al.  Evaluation of TRMM/GPM Blended Daily Products over Brazil , 2018, Remote. Sens..

[38]  Bon-Gang Hwang,et al.  Trade-Level Productivity Measurement: Critical Challenges and Solutions , 2013 .

[39]  L. Baskoro,et al.  Labor Productivity and Foreign Direct Investment in the Indonesian Manufacturing Sector , 2019, Signifikan: Jurnal Ilmu Ekonomi.

[40]  Khaled El-Rayes,et al.  Change Orders Impact on Labor Productivity , 2005 .

[41]  Syuhaida Ismail,et al.  Structural Equation Model of the Factors Affecting Construction Labor Productivity , 2018 .

[42]  Min-Yuan Cheng,et al.  Predicting productivity loss caused by change orders using the evolutionary fuzzy support vector machine inference model , 2015 .

[43]  Yoonkyung Lee,et al.  Classification of Multiple Cancer Types by Multicategory Support Vector Machines Using Gene Expression Data , 2003, Bioinform..

[44]  Awad S. Hanna,et al.  Impact of Shift Work on Labor Productivity for Labor Intensive Contractor , 2008 .

[45]  Y. Everingham,et al.  Accurate prediction of sugarcane yield using a random forest algorithm , 2016, Agronomy for Sustainable Development.

[46]  G. Jenks.  The Data Model Concept in Statistical Mapping , 1967 .

[47]  M. Schmidt,et al.  Climate-sensitive modelling of site-productivity relationships for Norway spruce (Picea abies (L.) Karst.) and common beech (Fagus sylvatica L.) , 2010 .

[48]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[49]  Christopher Conrad,et al.  Modelling the Gross Primary Productivity of West Africa with the Regional Biomass Model RBM+, using optimized 250 m MODIS FPAR and fractional vegetation cover information , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[50]  N. Coops,et al.  Airborne laser scanning for modelling understory shrub abundance and productivity , 2016 .

[51]  Albert P.C. Chan,et al.  Forecasting construction manpower demand: A vector error correction model , 2007 .

[52]  Chunlu Liu,et al.  Construction labor productivity convergence: a conditional frontier approach , 2016 .

[53]  Aminah Robinson Fayek,et al.  Context Adaptation of Fuzzy Inference System-Based Construction Labor Productivity Models , 2018, Adv. Fuzzy Syst..

[54]  Aminah Robinson Fayek,et al.  Predicting Industrial Construction Labor Productivity using Fuzzy Expert Systems , 2005 .

[55]  Gary David Holt,et al.  Severity diagnosis of productivity problems - A reliability analysis , 1998 .

[56]  Amlan Mukherjee,et al.  Using Agent-Based Modeling to Study Construction Labor Productivity as an Emergent Property of Individual and Crew Interactions , 2009 .

[57]  A. Sakamoto,et al.  Does wage reflect labor productivity? A comparison between Brazil and the United States , 2018, Brazilian Journal of Political Economy.

[58]  Majed A. Alotaibi,et al.  Combining biophysical parameters, spectral indices and multivariate hyperspectral models for estimating yield and water productivity of spring wheat across different agronomic practices , 2019, PloS one.

[59]  Rouslan A. Moro,et al.  Support Vector Machines (SVM) as a Technique for Solvency Analysis , 2008 .

[60]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[61]  Giovanni Luca Christian Masala,et al.  A comparative study of K-Nearest Neighbour, Support Vector Machine and Multi-Layer Perceptron for Thalassemia screening , 2003 .

[62]  Wen Yi,et al.  Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity , 2018, Journal of Construction Engineering and Management.

[63]  Jonathan H. Westover,et al.  Enhancing long‐term worker productivity and performance , 2010 .

[64]  Farnad Nasirzadeh,et al.  Dynamic modeling of labor productivity in construction projects , 2013 .

[65]  A. Roy Duff,et al.  An expert‐simulation system for construction productivity forecasting: Conformation method used to illustrate the uncertainty associated with expert's knowledge , 1996 .

[66]  Mohamed Salem Nashwan,et al.  Construction labour productivity: review of factors identified , 2019, International Journal of Construction Management.

[67]  K. A. Kovach,et al.  What motivates employees? Workers and supervisors give different answers , 1987 .