Predicting Industrial Construction Labor Productivity Using Fuzzy Expert Systems

The objective of this technical note is to illustrate the application of fuzzy expert systems to the modeling of a practical problem—that of predicting the labor productivity of two common industrial construction activities: rigging pipe and welding pipe. This note illustrates how to develop and test such a model, given the realistic constraints of subjective assessments, multiple contributing factors, and limitations on data sets. The factors that affect the productivity of each activity are identified, and fuzzy membership functions and expert rules are developed. The models are validated using data collected from an actual construction project. The resulting models are found to have high linguistic prediction accuracies. This note is of relevance to researchers by demonstrating how a fuzzy expert system can be developed and tested. It is of relevance to industry practitioners by illustrating how fuzzy logic and expert systems modeling can be exploited to help them solve real world problems.     

Relationship between Automation and Integration of Construction Information Systems and Labor Productivity

Information technology (IT) has been used to increase automation and integration of information systems on construction projects for over two decades. However, evidence that overall costs have been reduced or project performance has been improved with IT in construction is limited and mostly focused on application specific studies. A comprehensive understanding of the relationship between IT and project performance helps industry practitioners better understand the likely outcomes of implementation of IT application and likewise benefits researchers in improving the effectiveness in their IT development efforts. An opportunity to examine new evidence exists with the emergence of the Construction Industry Institute’s Benchmarking and Metrics database on construction productivity and practices. This article presents an analysis of that data to determine if there is a relationship between labor productivity and level of IT implementation and integration. Data from industrial construction projects are used to measure the relationships between the automation and integration of construction information systems with productivity. Using the independent sample t-test, the relationship was examined between jobsite productivity across four trades (concrete, structural steel, electrical, and piping) and the automation and integration of various work functions on the sampled projects. The results showed that construction labor productivity was positively related to the use of automation and integration on the sampled projects.     

Factors Affecting Engineering Productivity

Engineering performance has a major impact on subsequent project phases, such as procurement and construction, and thus, has the potential to affect the overall project outcome. This study utilizes metrics and a database from the Construction Industry Institute (CII) benchmarking and metrics program to investigate relationships between factors thought to affect direct engineering labor productivity during detailed engineering. Collaborating with industry practitioners, quantitative assessments were analyzed with industry input through various CII committee meetings and industry forums. Significant correlations are found between engineering productivity and project size, project type, project priority, and phase involvement. Correlations are also found between degree of modularization, funded front-end planning effort, and quality management and engineering productivity. These findings extend and, in some cases, contradict previous research.     

SERC—A Model for Estimating Construction Inputs

One of the main problems in the process of design and management of construction projects is obtaining accurate information for preliminary estimates. This information is crucial for the development of integrated systems for construction management because of the relationship between construction input data and subjects such as estimating, cost control, scheduling, resource management, etc. Existing methods for estimating input that originated in industrial engineering are inadequate for the unique conditions of the construction industry. The model described in this paper applies statistical analysis of data from past projects, and enables the user to estimate the data needed for the construction of a new project. The model is based on the following components: Project items and their quantities; inputs needed to produce those items; and factors that affect inputs of a specific project. The model equation was calculated using multiple regression techniques. The paper concludes with a case study of a construction input configuration for a concrete structure.     

Cost Information Model for Managing Multiple Projects

Construction companies must deal with several projects at once, but a system to manage multiple projects is not fully developed yet. The first step towards developing such system is to design an information model that is suitable for managing multiple projects. This paper presents the cost-based project modeling (CBPM) method in contrast to the traditional activity-based project modeling methods. The CBPM uses cost as a core of the model along with other project information organized around it. The CBPM serves as a platform for integrating project information from multiple projects. Various types of construction costs are hierarchically modeled to generate corporate-wide information such as project performances, cash flows, and other predictive indicators. Based on the information model, an object-oriented database was developed to contain cost data across several projects. In the model, a module that connects to external systems is built into the model to enhance interactivity with the legacy systems and the industry standards. A prototype system was developed and tested with actual project data to validate the information processing capabilities of the model. The findings from the test indicate construction cost can be an excellent medium that can organize various types of information of multiple projects.     

Models for Predicting Project Performance in China Using Project Management Practices Adopted by Foreign AEC Firms

China is a new market to many international architectural, engineering, and construction (AEC) firms and it is not known what would be the likely project outcomes, based on different project management (PM) practices adopted. This research developed and tested five models to predict the likely project success levels, based on PM practices adopted by foreign AEC firms in China. Based on data obtained from 33 projects, multiple linear regression (MLR) models for predicting the performance of foreign managed projects in China were constructed. The models were tested against 13 new cases, and the results show that they are able to predict project outcomes with some level of accuracy. The models show that certain scope management practices can be used to predict owner satisfaction, profit margin, and cost and quality performance of the project. Construction industry practitioners who are managing projects in China may benefit from the findings by focusing more on upstream management, like managing project scope, in order to ensure project success. It is recommended that construction industry practitioners use the MLR models to make preliminary assessment of the possibility of project success based on the type of PM practices they intend to adopt in China. From the results, they can then decide if they should change their practices or abort the project.     

Quantifying Engineering Project Scope for Productivity Modeling

A poor scope definition in an engineering design project disrupts project rhythm, causes rework, increases project time and cost, and lowers the productivity and morale of the workforce. A quantitative measurement of the project scope is the basis for productivity modeling that involves the measurement, estimation, control, and evaluation of productivity. This paper proposes a conceptual model, the quantitative engineering project scope definition (QEPSD), to standardize the measurement of engineering project scope in construction projects, within a computer aided design environment. The QEPSD quantitatively measures engineering project scope, in terms of the complexity of design items by defining design categories and complexity functions appropriate to the particular discipline. The proposed method was originally verified and implemented specifically for steel drafting projects. Actual data was analyzed and used to demonstrate the benefits of historical data prepared using QEPSD for project scope definition. It was found that the new method led to increased utilization of previously untapped values in historical data, improving the accuracy of project scope definition, and productivity modeling. The paper concludes with a discussion of the potential benefits of adopting the QEPSD method, and its implications upon various project management functions.     

Structural Equation Model of Construction Contract Dispute Potential

This paper presents the results of a structural equation model (SEM) for describing and quantifying the fundamental factors that affect contract disputes between owners and contractors in the construction industry. Through this example, the potential impact of SEM analysis in construction engineering and management research is illustrated. The purpose of the specific model developed in this research is to explain how and why contract related construction problems occur. This study builds upon earlier work, which developed a disputes potential index, and the likelihood of construction disputes was modeled using logistic regression. In this earlier study, questionnaires were completed on 159 construction projects, which measured both qualitative and quantitative aspects of contract disputes, management ability, financial planning, risk allocation, and project scope definition for both owners and contractors. The SEM approach offers several advantages over the previously employed logistic regression methodology. The final set of structural equations provides insight into the interaction of the variables that was not apparent in the original logistic regression modeling methodology.     

Highway Construction Data Collection and Treatment in Preparation for Statistical Regression Analysis

Currently, there is not an understanding of the project factors having a statistically significant relationship with highway construction duration. Other industry sectors have successfully used statistical regression analysis to identify and model the project parameters related to construction duration. While the need is seen for such work in highway construction, there are very few studies which attempt to identify duration-influential parameters and their relationship with the highway construction duration. The purpose of this work is to describe the highway construction data needed for such a study, identify a data source, collect early-design project data, and prepare the data for statistical regression analysis. The Virginia Department of Transportation is identified as the optimal data source. The data collected include historical contract and project level parameters. To prepare for statistical regression analysis, the contract duration collected is converted to construction duration by a seasonal adjustment process which removes historically typical nonworking days.     

Quantifying the Impact of Schedule Compression on Labor Productivity for Mechanical and Sheet Metal Contractor

In a typical construction project, a contractor may often find that the time originally allotted to perform the work has been severely reduced. The reduction of time available to complete a project is commonly known throughout the construction industry as schedule compression. Schedule compression negatively impacts labor productivity and consequently becomes a source of dispute between owners and contractors. This paper examines how schedule compression affects construction labor productivity and provides a model quantifying the impact of schedule compression on labor productivity based on data collected from 66 mechanical and 37 sheet metal projects across the United States. The model can be used in a proactive manner to reduce productivity losses by managing the factors affecting productivity under the situation of schedule compression. Another useful application of the model is its use as a litigation avoidance tool after the completion of a project.     

Impact of Change Orders on Labor Efficiency for Electrical Construction

Change orders are a source of many disputes in today's construction industry. The issue at hand is whether or not the execution of change orders work has a negative impact on overall labor efficiency on a construction project. Previous literature demonstrates evidence that change orders affect labor efficiency. Attempts have been made to quantify these impacts by many researchers, with limited success. Using the electrical construction industry, a research study has been conducted to quantify the impacts of change orders on labor efficiency. In this paper, results of hypothesis testing and regression analysis are presented. A linear regression model that estimates the loss of efficiency, based on a number of independent variables, is also presented. The independent variables used in this model are (1) qualitative and quantitative criteria used to determine whether projects are impacted by changes or not; (2) the estimate of change order hours for the project as a percentage of the original estimate of work hours; (3) the estimate of change order hours for the project; and (4) the total number of years that the project manager had worked in the construction industry. Additional projects were used to validate the model, with an average error rate of 5%. The results of this research study are useful for owners, construction managers, general contractors, and electrical specialty contractors, because they provide a means to estimate the impact of a change order under certain project conditions. This research also identifies factors, which, when understood and effectively managed, may be used to mitigate the impact of a change order on project costs and efficiency.     

Contractor Selection Criteria: Investigation of Opinions of Singapore Construction Practitioners

The construction industry has witnessed the failure of many contractors due to varying reasons such as financial problems, poor performance, or accidents arising from the lack of adequate safety consideration at worksites. All these incidents have led to the impression that the current system of awarding the contracts is inefficient in selecting the contractor capable of meeting the demands and challenges of present times and hence needs to be reviewed accordingly. Therefore, in an attempt to investigate the current situation of the Singapore construction industry a questionnaire survey was conducted for accruing the data required to identify the important contractor selection criteria (CSC) and to draw upon construction practitioners’ opinions regarding the importance of those CSC in assessing the capabilities of the candidate contractors during the selection process. The research reported upon forms part of a larger study that aims to develop a computer-interactive multicriteria decision system for contractor selection involving identification of CSC for inclusion in the system, investigation of CSC preferences of construction practitioners, and establishment of weights for those CSC from their perceived importance determined through the questionnaire survey of Singapore construction practitioners. The study highlights that there are statistically significant differences in opinions regarding the degree of importance assigned to some CSC among public clients, private clients, and contractors. Findings from the study may act as an aid in improving the Singapore construction industry by helping construction clients identify multiple CSC apart from cost which should always be considered when assessing the capability of candidate contractors during the selection process, by assisting contractors in improving their attributes in line with clients’ preferences and by facilitating Singapore construction clients and researchers to develop a contractor selection system capable of assessing multiple attributes of the candidate contractors so that the risk of the project failure due to the selection of an inappropriate contractor is minimized.     

Decision Tree Approach to Classify and Quantify Cumulative Impact of Change Orders on Productivity

Multiple or unusual change orders often cause productivity losses through a “ripple effect” or “cumulative impact” of changes. Many courts and administrative boards recognize that there is cumulative impact above and beyond the change itself. However, determination of the impact and its cost is difficult due to the interconnected nature of construction work and the difficulty in isolating causal factors and their effects. As a result, it is very difficult for owners and contractors to agree on equitable adjustments for the cumulative impact. What is needed is a reliable method (model) to identify and quantify the loss of productivity caused by the cumulative impact of change orders. A number of studies have attempted to quantify the impact of change orders on the project costs and schedule. Many of these attempted to develop regression models to quantify the loss. However, traditional regression analysis has shortcomings in dealing with highly correlated multivariable data. Moreover, regression analysis has shown limited success when dealing with many qualitative or noisy input factors. Classification and regression tree methods have the ability to deal with these complex multifactor modeling problems. This study develops decision tree models to classify and quantify the labor productivity losses that are caused by the cumulative impact of change orders for electrical and mechanical projects. The results show that decision tree models give significantly improved results for classification and quantification compared to traditional statistical methods in the field of construction productivity data analysis, which is characterized by noisiness and uncertainty.     

Relationship between Changes in Material Technology and Construction Productivity

There have been substantial changes in both material technology and construction productivity over the past several decades. By analyzing the changes in both material technology and productivity among 100 construction activities from 1977 to 2004, this research examines the strength and types of relationships that exist within these two occurrences. Through analysis of variance (ANOVA) and regression analyses, the researchers found that activities experiencing significant changes in material technology have also experienced substantially greater long-term improvements in both their labor and partial factor productivity. The research did find that a stronger relationship exists between changes in material technology and partial factor productivity than in labor productivity. The research also found that changes in the unit weight of materials had a significant relationship to labor productivity, while changes in installation and modularity had a significant relationship to partial factor productivity. The research findings will help industry practitioners to better understand how they may leverage technology to improve construction productivity, while also helping researchers understand the theoretical relationships between technology and construction productivity.     

Effects of Schedule Pressure on Construction Performance

Accelerating a project can be rewarding. The consequences, however, can be troublesome if productivity and quality are sacrificed for the sake of remaining ahead of schedule, such that the actual schedule benefits are often barely worth the effort. The tradeoffs and paths of schedule pressure—and its causes and effects—are often overlooked when schedule decisions are being made. This paper analyzes the effects that schedule pressure has on construction performance, and focuses on tradeoffs in scheduling. A research framework has been developed using a causal diagram to illustrate the cause-and-effect analysis of schedule pressure. An empirical investigation has been performed by using survey data collected from 102 construction practitioners working in 38 construction sites in Singapore. The results of this survey data analysis indicate that advantages of increasing the pace of work—by working under schedule pressure—can be offset by losses in productivity and quality. The negative effects of schedule pressure arise mainly by working out of sequence, generating work defects, cutting corners, and losing the motivation to work. The adverse effects of schedule pressure can be minimized by scheduling construction activities realistically and planning them proactively, motivating workers, and by establishing an effective project coordination and communication mechanism.     

Impact of Change’s Timing on Labor Productivity

There are many types of construction changes and each type can have an effect on labor productivity. To a certain extent though the specific type of change is not as important as the mere presence of the change and, as analyzed in this paper, the timing of that change. The research reported in this paper reaffirms that project change is disruptive and detrimental to labor productivity. Data from 162 construction projects were statistically analyzed and a series of three curves are presented in this paper, representing the impact that change has on the labor productivity for early, normal, and late timing situations. The projects are a representative sample of the industry, involving a wide range of sizes, different delivery systems, and industry sectors. Late change is more disruptive of project productivity than early change, all other things being equal. The implications and benefits of this research are clear: if changes are necessary, they should be recognized and incorporated as early as possible. Practitioners can use these data and curves for either forward pricing or retrospective pricing of changes. Other researchers can use these findings to test their own findings and to explore timing issues in further detail.     

Cumulative Effect of Project Changes for Electrical and Mechanical Construction

Change is inevitable on construction projects, primarily because of the uniqueness of each project and the limited resources of time and money that can be spent on planning, executing, and delivering the project. Change clauses, which authorize the owner to alter work performed by the contractor, are included in most construction contracts and provide a mechanism for equitable adjustment to the contract price and duration. Even so, owners and contractors do not always agree on the adjusted contract price or the time it will take to incorporate the change. What is needed is a method to quantify the impact that the adjustments required by the change will have on the changed and unchanged work. Owners and our legal system recognize that contractors have a right to an adjustment in contract price for owner changes, including the cost associated with materials, labor, lost profit, and increased overhead due to changes. However, the actions of a contractor can impact a project just as easily as those of an owner. A more complex issue is that of determining the cumulative impact that single or multiple change orders may have over the life of a project. This paper presents a method to quantify the cumulative impact on labor productivity for mechanical and electrical construction resulting from changes in the project. Statistical hypothesis testing and correlation analysis were made to identify factors that affect productivity loss resulting from change orders. A multiple regression model was developed to estimate the cumulative impact of change orders. The model includes six significant factors, namely: Percent change, change order processing time, overmanning, percentage of time the project manager spent on the project, percentage of the changes initiated by the owner, and whether the contractor tracks productivity or not. Sensitivity analysis was performed on the model to study the impact of one factor on the productivity loss (%delta). The model can be used proactively to determine the impacts that management decisions will have on the overall project productivity. They may also be used at the conclusion of the project as a dispute resolution tool. It should be noted that every project is unique, so these tools need to be applied with caution.     

Development and On-Site Evaluation of an Automated Materials Management and Control Model

Materials resources constitute a large portion of a project’s total cost and this makes them an important and attractive subject to control. Proper control and management of materials can meaningfully increase productivity by 6%, or more. A model based on automatic, or semiautomatic, data collection for materials management and control was developed. Based on project plans, the model initiates and manages the ordering of materials automatically and monitors both the actual flow of materials and the current stock at the construction site. The model permits real-time control, enabling corrective actions to be taken. In this manner, costs and unnecessary handling of materials are reduced. In addition, up-to-date information regarding materials flow is available and different statistical analyses are enabled: materials acquired from a specific supplier; materials used for a specific activity; and materials used during a specific month, etc. The information generated by the model enables the updating of a historical database to be used for planning of future projects.     

Impact of Extended Overtime on Construction Labor Productivity

This paper presents an analysis of the impacts of extended duration overtime on construction labor productivity. The results show a decrease in productivity as the number of hours worked per week increase and/or as project duration increases. The research focuses on labor intensive trades such as the electrical and mechanical trades. Overtime in this research is defined as the hours worked beyond the typical 40 h scheduled per week. The paper begins by presenting the effects of overtime and the need for an updated overtime productivity model. Data for the quantitative analysis was collected from 88 projects located across the United States by means of a questionnaire. Various statistical analysis techniques were performed to develop quantitative relationship curves, including multiple regression, P-value tests, and analysis of variance.