Construction can be considered a socio-technical system, which is challenging to model due to the many agents interacting either in a managed way or autonomously. Therefore, cause and effect models are hard to validate, and a traditional correlation approach is insufficient. In this study, the method of robustness testing was applied to test the effect stability when assumptions of a model are changed. The research objective is to apply robustness testing on WS data to assess the robustness and validity of the WS method. An actual refurbishment project was the case for this study, where data was acquired through nine days of continuous WS application. Time-series data were grouped into Direct Work (DW), Indirect Work, and Waste Work. Several different robustness tests were applied. It can be concluded that the WS method is robust, i.e., the effect (DW) is stable even if the assumptions are changed severely. Deleting 90% of the sample does, for instance, almost not change the effect. Likewise, if errors are infused into the sample, the effect is stable. Also, if certain structural parts are excluded from the sample, e.g., observations during morning startup, etc., the effect is still stable.
The European Green Deal's Renovation Wave aims to renovate 35 million energy-inefficient buildings to reduce carbon dioxide (CO2) emissions by at least 55% by 2030. Historically, efforts to reduce CO2 emissions focused on Operational Energy (OE) of the finished buildings. However, in recent years the Embodied Energy (EE) of the building’s construction process has gained attention because of its essential role in construction renovations projects. In this context, construction efficiency, and more precisely, workers’ efficiency, is a vital catalyst to achieve the European Union (EU) targets. To identify the impact of Construction Labour Productivity (CLP) on the renovation wave an exploratory case study was adopted as a research strategy. Data from four domestic housing renovation projects were gathered. Three specific research goals are outlined. The first is to demonstrate the effect of the adoption of Lean tools and methods to increase CLP. The second is to quantify the correlation between improved productivity and the EE emissions saved during the construction phase. The third goal is to estimate the effect the higher productivity has on OE emissions. The results show that the adoption of several Lean tools and methods has a potential to improve CLP to 45%. This rate of improvement for the 35 million housing units to be renovated could save 6.9 million tonnes CO2e from EE and 386 million tonnes CO2e from OE. This novelty link between process improvements and reduced energy consumption and emissions can support politicians and infrastructural developers in decision-making for a more sustainable construction industry.
The Work Sampling (WS) technique has been used in the construction industry since the 1960s to understand how workers spend their time. However, the WS categories have exhibited variation throughout history due to interpretation and application discrepancies. This lack of consensus on what represents Value-Adding-Work (VAW) and Non-Value-Adding-Work (NVAW), has hindered the use of data from previous WS studies for further analysis. For this reason, this research aims to understand how the data obtained from the WS application can be analyzed to discuss value. To address this question, the authors adopted a case study as the primary research strategy. The phenomenon of the present study comprises the activities involved in the renovation process in residential buildings. The phenomenon is studied through the application of the WS technique. The authors adopted previous analyses from the existing literature and proposed new types of analyses. The discussion section presents various kinds of analysis based on a breakdown of categories into codes: (1) general analysis; (2) a category breakdown analysis; (3) one single component/material analysis; (4) recategorized activities analysis; and (5) correlation analysis. The proposal of a detailed code classification, named breakdown work sampling, represents the main novelty of this study.
This study is part of an ongoing research project aiming to develop a method for understanding workers’ efficiency (workers’ time spent in value-adding activities) by measuring new indexes, such as workers’ travelled distances and workers’ locations collected by smartwatches. To achieve the objective of the study, a Design Science Research (DSR) strategy was adopted. The first cycle consists of understanding which types of information smartwatches can collect and how this data can be employed for measuring workers’ efficiency. This paper reports a case study as part of the first Cycle of the DSR. The object studied were the activities carried out by a carpenter trade in a housing renovation project. The authors used the geographic coordinates obtained by smartwatches worn by the carpenter trade connected to two Global Navigations Satellite Systems. The primary contribution of this research consists of proposing a set of five guidelines for the application of smartwatches, using data gathered from the case study. The guidelines are: (1) adopt a stratified sampling approach for selecting the workers involved according to their tasks conducted; (2) set up the smartwatches considering workers’ physical features; (3) carefully consider the job site location for delivering the smartwatch to workers; (4) establish assumptions for the data cleaning process regarding construction project features and the study’s goal; and (5) use individual participant data in the analysis according to each participant’s characteristics and role.
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