Modelling construction labour productivity from labour's characteristics

Labour is a fundamental input to any construction project to achieve the highest level of productivity. Productivity remains as one of the most important ways to measure the overall performance of construction project. Construction productivity is directly related to labour and thus, it is mainly dependent on human effort and performance. Improvement of Construction Labour Productivity (CLP) can directly help to improve the performance of construction companies, become more competitive, besides contributes to national economy. The aim of the research is to develop and introduce a new framework for systematic assessment of the factors influencing construction labour productivity and use the collected data to create models by applying state-of-art techniques and comparing the accuracies in predicting the labour productivity in construction. The scope of the study was limited to Malaysia only. A thorough literature survey was conducted to list the factors related to CLP with different studies throughout the globe. The factors were filtered using two-stage procedures - first the factors were shortlisted based on the relevance of labour and then a survey was conducted among project managers to rank the factors based on the importance of Malaysian context using a 3-point Likert scale on each factor. The ranks of the factors were analysed using statistical tools. The top class factors were identified using Jenks Optimization Techniques. The classified CLP factors were used to design a field survey to collect data from construction workers. Five state-of-arts of models were developed to predict the CLP from the factors including three data mining models, one conventional model and one multi-criteria model. Salary of labour was considered as a proxy to the productivity to develop the models. The performance of the models were assessed using five categorical indices. The results of literature review revealed that a total of 112 factors related to productivity in construction industry have been identified throughout the globe. Ten factors were identified through the analysis of preliminary survey data using different methods. Among them, seven factors were found common for all the methods which were identified as the important CLP factors for Malaysian construction industry. The factors are (1) Lack of Work Experience (2) Job Category (3) Education/Training (4) Nationality (5) Worker Skills (6) Age and (7) Marital Status. The relative performance of different models was compared to identify the best model in term of the rate of accuracy in prediction of labour productivity. Data mining models were found to perform better compared to other models. The Percentage of Correct (PC) for data mining models were found in the range of 0.735-0.835, Probability of Detection (POD) between 0.741 and 0.911, Heidke Skill Score (HSS) between 0.792 and 0.802 and Peirce Skill Score (PSS) in the range of 0.792 to 0.799, while the False Alarm Ratio (FAR) were found in the range of 0.102 to 0.279. The values were found better than that obtained using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) (PC=0.739, POD=0.740, HSS=0.794, PSS=0.725 and FAR=0.256) and much better than that obtained using Linear Regression (LR) (PC=0.577, POD=0.618, HSS=0.533, PSS=0.498 and FAR=0.533). Among the data mining models, Support Vector Machine (SVM) was found to provide the best results in term of all statistical metrics used. The POD for SVM was found above 90% in predicting different categories of productivity. The method discussed in this research can serve as a newly developed framework to predict the level of construction labour productivity for project

Modelling construction labour productivity from labour characteristics

Labour is a fundamental input to any construction project to achieve the highest level of productivity. Productivity remains as one of the most important ways to measure the overall performance of construction project. Construction productivity is directly related to labour and thus, it is mainly dependent on human effort and performance. Improvement of Construction Labour Productivity (CLP) can directly help to improve the performance of construction companies, become more competitive, besides contributes to national economy. The aim of the research is to develop and introduce a new framework for systematic assessment of the factors influencing construction labour productivity and use the collected data to create models by applying state-of-art techniques and comparing the accuracies in predicting the labour productivity in construction. The scope of the study was limited to Malaysia only. A thorough literature survey was conducted to list the factors related to CLP with different studies throughout the globe. The factors were filtered using two-stage procedures - first the factors were shortlisted based on the relevance of labour and then a survey was conducted among project managers to rank the factors based on the importance of Malaysian context using a 3-point Likert scale on each factor. The ranks of the factors were analysed using statistical tools. The top class factors were identified using Jenks Optimization Techniques. The classified CLP factors were used to design a field survey to collect data from construction workers. Five state-of-arts of models were developed to predict the CLP from the factors including three data mining models, one conventional model and one multi-criteria model. Salary of labour was considered as a proxy to the productivity to develop the models. The performance of the models were assessed using five categorical indices. The results of literature review revealed that a total of 112 factors related to productivity in construction industry have been identified throughout the globe. Ten factors were identified through the analysis of preliminary survey data using different methods. Among them, seven factors were found common for all the methods which were identified as the important CLP factors for Malaysian construction industry. The factors are (1) Lack of Work Experience (2) Job Category (3) Education/Training (4) Nationality (5) Worker Skills (6) Age and (7) Marital Status. The relative performance of different models was compared to identify the best model in term of the rate of accuracy in prediction of labour productivity. Data mining models were found to perform better compared to other models. The Percentage of Correct (PC) for data mining models were found in the range of 0.735-0.835, Probability of Detection (POD) between 0.741 and 0.911, Heidke Skill Score (HSS) between 0.792 and 0.802 and Peirce Skill Score (PSS) in the range of 0.792 to 0.799, while the False Alarm Ratio (FAR) were found in the range of 0.102 to 0.279. The values were found better than that obtained using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) (PC=0.739, POD=0.740, HSS=0.794, PSS=0.725 and FAR=0.256) and much better than that obtained using Linear Regression (LR) (PC=0.577, POD=0.618, HSS=0.533, PSS=0.498 and FAR=0.533). Among the data mining models, Support Vector Machine (SVM) was found to provide the best results in term of all statistical metrics used. The POD for SVM was found above 90% in predicting different categories of productivity. The method discussed in this research can serve as a newly developed framework to predict the level of construction labour productivity for project

Climate change projection and drought characterization in Bangladesh

One of the biggest threats of the climatic change is aberrant pattern or distribution of rainfall that results to drought. The main objective of this research was to develop a methodological framework to assess the impacts of climate change on seasonal drought characteristics with uncertainty. Bangladesh, one of the most vulnerable countries in the world to climate change was considered as the study area for implementation of the framework. An ensemble of general circulation models (GCMs) of Coupled Model Intercomparison Project phase 5 (CMIP5) were used for downscaling and projection of rainfall and temperature under different Representative Concentration Pathways (RCP) scenarios. Two state of art data mining approaches known as Random Forest (RF) and Support Vector Machine (SVM) were used for the development of downscaling models and Quantile Mapping (QM) approach was used to remove biases in GCMs. The observed and future projected rainfall data were used to characterize the seasonal droughts using Severity-Area-Frequency (SAF) curves developed for different climatic and major crop growing seasons. The results revealed superior performance of SVM in downscaling rainfall and temperature in tropical climate in terms of all standard statistics. Downscaling of CMIP5 GCMs projections revealed a change in annual average rainfall in Bangladesh in the range of -8.6% in the northeast to +11.9% in the northwest, which indicates that spatial distribution of rainfall of Bangladesh will be more homogeneous in future. The maximum and minimum temperatures of Bangladesh were projected to increase in the range of 0.8 to 4.3ºC and 1.0 to 4.8ºC, respectively under different RCPs. Future projection of droughts revealed that affected areas will increase for higher severity and higher return period droughts. Overall, the country will be more affected by higher return period Kharif (May- October) and monsoon droughts, and lower return period pre-monsoon and postmonsoon droughts due to climate change