End-to-end GRU model for construction crew management

Crew management is critical towards improving construction task productivity. Traditional methods for crew management on-site are heavily dependent on the experience of site managers. This paper proposes an end-to-end Gated Recurrent Units (GRU) based framework which provides site managers a more reliable and robust method for managing crews and improving productivity. The proposed framework predicts task productivity of all possible crew combinations, within a given size, from the pool of available workers using an advanced GRU model. The model has been trained with an existing database of masonry work and was found to outperform other machine learning models. The results of the framework suggest which crew combinations have the highest predicted productivity and can be used by superintendents and project managers to improve construction task productivity and better plan future projects

Predicting construction productivity with machine learning approaches

Machine learning (ML) is a purpose technology already starting to transform the global economy and has the potential to transform the construction industry with the use of data-driven solutions to improve the way projects are delivered. Unrealistic productivity predictions cause increased delivery cost and time. This study shows the application of supervised ML algorithms on a database including 1,977 productivity measures that were used to train, test, and validate the approach. Deep neural network (DNN), k-nearest neighbours (KNN), support vector machine (SVM), logistic regression, and Bayesian networks are used for predicting productivity by using a subjective measure (compatibility of personality), together with external and site conditions and other workforce characteristics. A case study of a masonry project is discussed to analyse and predict task productivity

Does Compatibility of Personality Affect Productivity? Exploratory Study with Construction Crews

Crew productivity is a function of how efficiently labor is utilized in the construction process. However, previous research in construction has not comprehensively investigated the relationship between personality and crew productivity. This paper uses personality profiles to investigate a new fundamental concept, the relationship between compatibility of personality and crew productivity at the task level. Twenty-eight masons completed a revised questionnaire of the Big Five to indicate their personality. Personality scores were used to calculate compatibility in each of the 20 participating two-mason crews working on eight projects. Regression analysis was performed to establish the relationship between compatibility and crew productivity. Results show that that there is a high positive correlation between compatibility and crew productivity. Compatibility accounts for more than half of the predictable variance in productivity. This paper makes four major contributions: it proposes a new metric to measure compatibility of personality among workers in a crew; it reveals how personality factors affect productivity; it provides rigorous methods to analyze correlations (using confidence intervals and Bayesian inference) for construction experiments; and it provides theoretical contributions to advancing the theory of personality and productivity in construction projects

Multi-skilled Labor Optimization with Partial Allocation of Resources

The current practice of labor allocation in construction schedules assumes single-skilled workforce; meaning that each worker is assumed to be skilled in only one trade. In such practice, at any instance in the project lifecycle, some of the workforce become idle waiting for other labor types to complete their work. Traditionally, companies may relocate idle workers to other projects and return them back to their original project when needed again. This complicates the resource management process and is not often performed successfully, leading to schedule and cost overruns. Alternatively, project managers may keep the idle workforce at their projects because they will be needed at a later stage and pay them in their idle days, which adds unnecessary costs to the project. Another solution would be continuously hiring and laying off labor at need, which has severe negative impacts on projects and firms. Due to the inefficiencies of these solutions, some research discussed the idea of “multi-skilled” labor, where some of the workers may have enough training to carry out different activity types. Multi-skilling decreases inefficiencies and ensures a smooth and continuous progress of works whilst maintaining the workforce and keeping their idle time to a minimum. Multi-skilling could be also used to speed up progress in construction schedules. Previous research efforts have been made to encourage contractors in pursuing multiskilling as a solution to the non-smooth resource histograms. Yet, the literature falls short in providing a robust multi-skilling framework; specifically, one that considers the cost of training labor and solves the partial allocation problem. The objective of this research is to improve project duration and minimize unnecessary costs through the utilization of multi-skilled labor. Through a multi-step methodology, a model that optimizes the allocation of multi-skilled labor resources was developed. The novelty of the presented model is that it further minimizes the idle times of labor when compared to previous multi-skilled labor models, due to its capability in allocating resources “partially” to segments of activities rather than to full activities. In other words, unlike previous models, the developed model recognizes the fact that a crew can work for a period of time in an activity, then some workers in that crew can be allocated to another activity, leaving the rest of the crew to complete the first activity. The model allows the user to enter any number of activities and up to ten different resource types. With the use of genetic algorithms idle resources are assigned to activities that require additional manpower in order to reduce their durations, and in turn reduce the project’s indirect costs. When applied to a case study, the model generated promising results, where the reduction in duration between the single skilled allocation and multi-skilled labor allocation was 31% and this reduction jumped to 44% when partial allocation was applied. Multiskilling did not only reduce the idle labor days, but it will also shift the resource usage histogram’s end point to the left, reducing the total project duration. This did not only reduce the unnecessary costs being paid to workers on days where they have no work, but it also reduced the total indirect costs which are directly proportional to the overall project duration

Comprehensive Analysis of the Multiskilling Strategy Among Craft Professionals in the Construction Industry

Purpose: To explore detailed dynamics of the multiskilling labor strategy as it has evolved over the past two decades as a possible solution to alleviate the labor shortage in the construction sector. Problem: The strategy has been adopted by many organizations and individuals in the US construction workforce, yet there is no existing study investigating the level of actual certification levels of the multiskilled craft professionals across multiple trades and how multiskilled craft professionals evolve. Moreover, limitations exist in understanding the motivation behind becoming multiskilled and measuring the competency level among multiskilled craft professionals. A good amount of research into understanding the contribution of the multiskilled craft professionals is a necessity that will shed light on its impact of the shortage of the workforce on the construction sectors.&nbsp; Background: Since the 1980s, the construction sector has experienced a nationwide shortage of skilled craft professionals. To alleviate this problem, the multiskilling strategy was introduced as a possible solution to reduce the labor shortage that is confronting the US construction industry. The strategy involves training and certification of craft professionals to perform more tasks across multiple trades in addition to their primary duties in the workplace. There is a need to assess and evaluate current levels of multiskilling craft professionals and compare it to singleskilled professionals for successful implementation.&nbsp; Intellectual Merit: This study intends to address the problems of the construction sectors through the following objectives: - Measure the level of certification achieved among multiskilled craft professionals; - Comprehend how multiskilled craft professionals have evolved in the last decade; - Examine the factors that motivate individuals to adopt the multiskilling strategy; and - Examine the competency levels, including the understanding of skill progression and conditions, among single-skilled and multi-skilled craft professionals;&nbsp; This dissertation offers a comprehensive understanding of the multiskilling strategy by focusing on four different data sources, including the following: 638,469 exam completions between 2005 to 2020 from the National Center for Construction Education and Research (NCCER) and its National Craft Assessment and Certification Program (NCACP), last access to the data 2020; - RT-370 Craft Survey: Online survey with a sample size of 2,740 construction -- craft professionals collected from all 50 states, 2021; - University of Texas at Austin 2000 Center for Construction Industry Studies Survey: Sample size of over 1100 craft professionals across the US, 2000; and - Interviews with multiskilled craft professionals within the construction industry sector from Northern Colorado.</p

Scheduling and staffing of multiskilling of workforce in the context of off-side construction

Aim: There is an increase of interest in multiskilling research from the academy, industry and governmental authorities. Multiskilling of a workforce refers to enhancing flexibility of production by enabling labor to be reallocated in response to change in production priorities during the production horizon. Production priorities can change for several reasons; however, this study considers changes due to alterations in bottleneck configurations. The aim of this research is to investigate the extent to which operational benefits associated with different multiskilled resource management policies pertaining to bottleneck configurations can be achieved in off-site construction. To achieve this aim, first the multiskilling of a workforce in an off-site construction context should be understood as it is a complex matter in both conception and application. Second, an appropriate scheduling method should be developed to allocate an existing workforce to the right tasks, based on their skill level and set, during the production makespan. Third, a staffing platform should be developed to facilitate recruiting and hiring of a multiskilled workforce with an appropriate skill level and set. Methodology: In the Chapter 2 a two-stage paper-screening methodology was used to collect relevant papers in the literature review section. A flow-shop-based optimization methodology is used in the Chapter 3 to schedule multiskilled crew during the production makespan to achieve the production objective. A quadratic resource allocation model was developed to allocate a workforce to different tasks with consideration of the scheduling cost. A piece-wise linearization method is deployed to linearize quadratic constraints and decrease solution time. The Chapter 4 adopts a hybrid method including optimization and multi-criteria decision-making techniques to advise the best multiskilling strategy by comparing the performance of existing multiskilling staffing configurations based upon a range of existing qualitative and quantitative criteria. PROMETHEE is recognized as a suitable multicriteria decision-making approach to incorporate qualitative criteria. A flow shop scheduling method is used to obtain an optimized performance from alternatives pertaining to quantitative criteria. The Chapter 5 of this thesis presents a decision-support tool to optimize a multiskilled staffing strategy. The methodology in this chapter differs from that in Chapter 3 in that the developed staffing optimization platform explores every possible multiskilling strategy to find the optimal staffing configuration. Findings: In Chapter 2, the literature review results in the development of a construction multiskilling framework. This framework investigates multiskilling literature in conception and application. Multiskilling framework includes four main categories of multiskilling context, collateral effects, Mainstream research and strategy. A developed scheduling platform in Chapter 3 indicates that an optimal multiskilled labor allocation can lead to significantly different outcomes in terms of cost and time, based upon whether the location of the bottleneck is fixed or variable. The findings in Chapter 4 indicate that chaining and hiring a multiskilled workforce which is able to contribute to four different tasks, are the best multiskilling staffing strategies among existing ones. Sensitivity analysis pertaining to different criteria weight illustrates that the results of this investigation are stable in a wide range of alterations in the weight allocation. In Chapter 5 the decision-support tool illustrates that the optimal multiskilling strategy is highly context specific and should be customized in relation to production circumstances and data, especially the magnitude of bottlenecks. A slight alteration in the production characteristics can lead to significant changes in the optimal cross-training policy. Subjective multiskilling of a workforce could lead to counterproductive results such as a significant cost overrun. Numerical experiments indicate that if there is no extra capacity to allocate more workers to a bottleneck workstation, multiskilling of the workforce in the workstation immediately preceding the bottleneck workstation can lead to enhancement in the productivity. Contribution: The main contribution of the Chapter 2 is to identify theoretical gaps in the cross-training research and pave the way for comprehensive studies to produce more realistic multiskilling knowledge that considers both technical and managerial details. Research findings in Chapter 3, contribute to the scheduling literature by presenting an optimization platform for multi-skilled resource allocation and relocation during the makespan pertaining to the project objective. Research findings in Chapter 4 contribute to staffing literature by presenting a hybrid methodology which can encompass qualitative criteria as well. Research findings in Chapter 5 contribute to staffing literature by presenting a novel optimization platform to optimize configuration of multiskilled labor pertaining to their skill set. Chapter 3, 4 and 5 make another important contribution to the body of knowledge which is quantifying how performance measures and labor skill sets interact with each other. The decision-support tool, which is incorporated in Chapter 5, can help off-site construction industry practitioners, without a relevant academic background, to staff and schedule a workforce to achieve their production objective

Improving labour productivity in construction. A hybrid machine learning approach

Achieving less than ideal productivity is a problem the construction industry faces in most advanced countries, including the UK. One way to change this is to improve on-site execution by, for example, more accurate planning of construction operations. Despite continuous efforts for automation, mechanisation, and off-site production, the construction industry can still be considered labour-intensive. Therefore, understanding labour productivity and the factors influencing it is vital to better planning. Owing to their versatility, durability, long service life, and being low maintenance, bricklaying works are ubiquitous, especially in housing and public projects, for example, schools. These operations are also especially labour-intensive. Consequently, an examination of bricklaying works is important for better planning and management of most construction projects. Ultimately, any gains in this operation could lead to an overall increase in site-based productivity. The aim of the research project is to provide a better understanding of the bricklaying process and how it can be modelled, descriptively and normatively, to find a modelling approach that allows for a better examination of the effects of various factors on bricklaying productivity. A number of factors influence on-site productivity. This research project focuses on those that are known in advance, in the pre-planning phase of the construction projects. These are the worker and wall characteristics. To analyse bricklaying operations, a hybrid model is created. The effects of the above-mentioned factors on labour productivity are investigated with the help of the artificial neural network component, while the discrete-event simulation part models the process of block- and bricklaying. The model is built and tested with the help of real-life data collected at two construction projects by conducting a traditional work study. When the productivity rates were measured, note was made of the bricklayer working on the course, and the wall section where they worked. Site supervisors filled in the questionnaires asking about operative characteristics, while the wall characteristics were determined based on the drawings and specifications. The resulting model can be used to provide more accurate productivity rate predictions for more precise time and cost estimates, and improved project planning in bricklaying

Crew Allocation System for the Masonry Industry

Masonry construction is labor-intensive. Processes require a large number of crews made up of masons with diverse skills, capabilities, and personalities. Often crews are reassembled and the superintendent in the site is responsible for allocating crews to balance between the complexity of the job and the need for quality and high production rates. However, the masonry industry still faces increased time and low productivity rates that result from inefficiencies in crew allocation. This article presents a system for efficient crew allocation in the masonry industry formulated as a mixed-integer program. The system takes into consideration characteristics of masons and site conditions and how to relate these to determine the right crew for the right wall to increase productivity. With the system, superintendents are not only able to identify working patterns for each of the masons but also optimal crew formation, completion times, and labor costs. To validate the model, data from a real project in the United States is used to compare the crew allocation completed by the superintendent onsite with the one proposed by the system. The results showed that relating characteristics of workers with site conditions had a substantial impact on reducing the completion time to build the walls, maximizing the utilization of masons, and outlining opportunities for concurrent work