Construction Phase
Sustainable Construction Research Group (SCRG)
University of Alberta, Department of Civil & Environmental Engineering
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Following Canada's 2030 emission reduction plan, the city of Edmonton has recently requested all contractors to report their GHG emissions. Therefore, our research focuses on a reliable and practical quantifying tool to predict the emissions caused by each construction operation before executing the project is essentially required to declare the amount of emissions caused by any contractor. The ability to quantify the emissions early, during the planning stage of any project, helps the project team to develop various execution scenarios and select the optimum one.
Ahmed Elnady and Ahmed Hammad
Multiple factors that impact project-based organizations’ workload fluctuation have already been identified by researchers. Although much effort has been devoted to finding these aspects, extant literature reviews lack systematic analysis and are confined to a few articles. This study tackles the lack of a systematic evaluation and content analysis of published studies related to workload fluctuation and offers statistics on the most prevalent variables in both the pre-award and post-award phases. The available body of knowledge is analyzed using the relative usage index (RUI) and social network analysis (SNA). RUI defines the gap in the frequency of modeling the variables. SNA defines the gap between mental models linking the variables affecting this problem and the applied dynamic models used to solve it. Results reveal some gaps, for instance, owner strictness and bid time have received very little attention in the literature.
Hamidreza Golabchi and Ahmed Hammad
Existing labor estimation models typically consider only certain construction project types or specific influencing factors. These models are focused on quantifying the total labor hours required, while the utilization rate of the labor during the project is not usually accounted for. This study aims to develop a novel machine learning model to predict the time series of labor resource utilization rate at the work package level. More than 250 construction work packages collected over a two-year period are used to identify the main contributing factors affecting labor resource requirements. Also, a novel machine learning algorithm – Recurrent Neural Network (RNN) – is adopted to develop a forecasting model that can predict the utilization of labor resources over time. This paper presents a robust machine learning approach for predicting labor resources’ utilization rates in construction projects based on the identified contributing factors. The machine learning approach is found to result in a reliable time series forecasting model that uses the RNN algorithm. The proposed model indicates the capability of machine learning algorithms in facilitating the traditional challenges in construction industry. The findings point to the suitability of state-of-the-art machine learning techniques for developing predictive models to forecast the utilization rate of labor resources in construction projects, as well as for supporting project managers by providing forecasting tool for labor estimations at the work package level before detailed activity schedules have been generated. Accordingly, the proposed approach facilitates resource allocation and enables prioritization of available resources to enhance the overall performance of projects.
Hady Elkholosy, Rana Ead, Ahmed Hammad, and Simaan AbouRizk
Accurate labor resource allocation ensures that tasks are assigned to the most suitable individual(s) and the optimal number of staff are available to complete certain tasks, making it essential for the success of construction projects. This study proposes a methodology for forecasting labor resource requirements for upcoming projects using data mining techniques. The framework consists of two components, a data acquisition model and a forecasting model. The data acquisition model provides a structured approach for tracking and storing project data, while the forecasting model uses the stored project data and applies machine learning algorithms to predict labor requirements. A case study is used to illustrate the application of the framework in actual projects. The results indicate the usefulness of machine learning in providing low error estimates compared to methods currently adopted in the industry. The results also revealed that forecasting accuracy considerably increases as the number of historical projects increases which signifies the importance of the data acquisition model. The models presented in this study are expected to help guide and promote the application of more accurate workforce forecasting techniques.
Mai Monir Ghazal and Ahmed Hammad
Currently, cost overrun is a global challenge to completing construction projects successfully. To overcome this problem, earlier studies investigated factors of cost overrun. Knowledge Discovery in Data (KDD) and data mining techniques have been implemented effectively in various research areas to extract novel and valuable knowledge from historical data but have only recently been implemented in the construction industry. This research aims to develop a model that predicts project cost overrun using a suitable data mining technique and cost overrun factors as predictors. A review of the literature identified twelve factors that can be easily measured and analyzed in construction projects. A case study was performed to validate the model with an actual data set of executed projects. The resulting model is simple, interpretable, and relatively accurate (60.87%), and it uses three steps of data mining – clustering, feature selection, and classification. These steps improve model performance.
Ahmed Hammad, Simaan AbouRizk, and Yasser Mohamed
Improper management of labor resources is one of the main causes of schedule delays and budget overruns in industrial construction projects. During management of these projects, a vast amount of data is collected and discarded without being analyzed to extract useful knowledge. To address this issue, an integrated proposed methodology is developed based on a five-step knowledge discovery in data (KDD) model. First, a synthesis of previous research is presented. Second, an inclusive analysis of the industrial construction domain and labor resources data is performed. Third, the concept of predefined progressable work packages is introduced for consistent data collection. Fourth, a prototype data warehouse is built using the snowflake schema to centrally store the collected data and produce dynamic online analytical processing (OLAP) reports and graphs. Fifth, data mining techniques are applied to extract useful knowledge from large sets of real projects’ data. Results show that the developed methodology is capable of gathering valuable knowledge from previously unanalyzed data that significantly improves current resource management practices.
It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data warehouse. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.
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