Delay Time Analysis in Microtunneling Projects

Delay in microtunneling projects is a complex multivariate problem. Delay in microtunneling is defined as the nonworking time of a microtunneling project due to any reason other than scheduled stops. There are many reasons for delay such as mechanical failure of system components, leakage of hydraulic hoses, blockage of slurry pipes, and waiting time for excavated materials hauling equipment. Delay time increases the project duration and consequently the project cost. Delay data were collected from 35 microtunneling projects. Collected delay data were delay duration, delay reason, time, and location from the start to the stopping point. Five categories of delay causes were used in the analysis. Prediction of delay time will enhance the estimation accuracy of microtunneling project duration. A predictive model using a probabilistic approach was selected to represent the delay time. Based on data characteristics, a Weibull distribution was determined to best represent the overall delay duration in microtunneling projects. Using “regression with life data,” expected overall delay in a microtunneling project could be predicted as a function of driven length. The model will help contractors to estimate total project time with reasonable accuracy. Knowing the anticipated delay time will allow contractors to have a point of comparison for actual performance.     

Soil Penetration Modeling in Microtunneling Projects

As the need for utility service line replacement or repairs with minimum disruption to the surface have increased, so has the demand for trenchless excavation methods, in particular, microtunneling. Microtunneling is a trenchless technique that is used to install new pipelines. Microtunneling can be applied in gravity and pressure lines, permanent ducts for cables, and crossings under rails or roads. When bidding a microtunneling project, the main concern of microtunneling contractors is predicting the underground behavior of the machine. In other words, the productivity of microtunneling is the key to profit in microtunneling projects. Contractors generally predict approximate productivity based on experience, which risks cost estimation accuracy for microtunneling projects. Contractors lack a productivity model that helps them to predict driving time. This paper is a part of a series of papers covering the productivity of microtunneling projects. This paper focuses on predicting the penetration time of the microtunneling machine.     

Modeling Microtunneling Projects using Computer Simulation

Tunnels projects are constructed to facilitate the execution of underground works with minor disturbance on surface structures and traffic. This is deemed important especially in downtown cities where disturbances should be minimized to assure flowability on surface and underground infrastructures. Microtunneling involves the use of a remotely controlled, guided pipe-jacking process in order to support excavation face. Microtunneling aids in avoiding the need of open trench for pipe laying, which causes extreme disruption to the surrounding. This paper presents a tool for planning microtunnels projects using computer simulation. The proposed tool aids contractors in planning microtunneling by estimating their associated time and cost of construction. There are six models that are coded in the proposed tool in order to capture the construction of microtunnels and shafts. The tool breaks down microtunnels projects into microtunnels segments and shafts which constitute several construction zones. An application example is presented to demonstrate the features of the proposed tool.     

Productivity Improvement: The Influence of Labor

Labor has a significant influence on construction productivity. The level of productivity is a result of the driving, induced, and restraining forces acting upon workers. These forces act positively and negatively with regard to productivity improvement. A framework for analyzing fhe influence of each of these forces on four major labor related determinants of construction productivity is presented. Approaches to productivity improvement are analyzed in terms of reducing the negative forces and strengthening the positive forces.     

Work Flow Variation and Labor Productivity: Case Study

Different types of flow variation and how they affect construction project performance have been studied by previous researchers. One aspect that has not been well researched is how work flow variation and labor productivity are related in construction practice. To study that issue, 134?weeks of project production data were collected and analyzed to explore this relationship. Labor productivity was found to be positively correlated with Percent Plan Complete (PPC), a measure of work flow variation. The relationship between productivity and the ratio of total task completion to planned tasks, weekly workload, weekly work output, and weekly work hours was also studied, and no significant correlation was found. The results suggest that productivity is not improved by completing as many tasks as possible regardless of the plan, nor from increasing workload, work output, or the number of work hours expended. In contrast, productivity does improve when work flow is made more predictable. These findings can help project managers focus on actual drivers of productivity. It can also help consulting companies pinpoint responsibility for productivity losses in claims.     

Latent Structures of the Factors Affecting Construction Labor Productivity

At any moment in time, a multitude of factors simultaneously impact construction productivity. Utilizing the knowledge of thousands of construction craft workers, the writers quantitatively analyzed the underlying structure of the factors affecting construction productivity and identified which factors the craft workers consider to be more relatively important as well. This research identified 83 factors affecting construction labor productivity through 18 focus groups with craft workers and their immediate supervisors on nine jobsites throughout the U.S. Next, a nationwide survey was administered to 1,996 craft workers to assess the impact of these factors on construction labor productivity. Principal factor analyses identified 10 latent factors to represent the underlying structure of 83 productivity factors. In addition, the relative importance of the factors’ impact on construction productivity was examined based on the crafts’ union status, trade, and position (craft worker versus foreman). The writers also compared their results to similar previous efforts, and more importantly, identified significant differences that may impact future productivity improvement strategies. This research will help industry and the research community better understand the factors affecting construction labor productivity and more effectively direct future efforts to improve its performance.     

Critical Investigation into the Applicability of the Learning Curve Theory to Rebar Fixing Labor Productivity

The learning phenomenon has proved applicable in various industries, especially those associated with mass productions such as aircraft and automobile assembly. The learning process in labor-intensive industries is assumed to be more significant in the sense that automated work is constrained by the fact that machines cannot benefit from previous experience and therefore would not “learn” to run any faster or increase the rate of production. Several previous investigations into the applicability of learning curve theory to the construction industry proved the importance of this concept to labor productivity. Nonetheless, a thorough examination of the literature revealed a dearth of research into the effect of learning on the rebar fixing trade. Therefore, the objective of this research is to explore the influence of recurring building floor configurations on rebar fixing labor productivity. To achieve this objective, fixing labor inputs of beams and slabs from 21 multistorey residential building floors were collected and analyzed using the straight-line learning curve model. Fixing labor inputs for each cycle, i.e., floor, and its associated cycle number were modeled using the least-squares method. According to the learning curve theory, labor inputs are expected to decrease by a certain percentage as the cycle number increases. Contrary to the basic principle underlying the theory, the majority of buildings observed exhibited either an increase or a negligible reduction in labor inputs as the cycle number of recurring floors increased.     

Impact of Shift Work on Labor Productivity for Labor Intensive Contractor

Generally, a contractor has three options in accelerating a construction schedule: working longer hours, increasing the number of workers, or creating an additional shift of workers. There has been a significant amount of research conducted on scheduled overtime on construction labor productivity. However, little information has been found in the literature addressing the labor inefficiency associated with working a second shift. This paper has qualitative and quantitative components. The qualitative part details why and how shift work affects labor productivity, and then addresses the appropriate use of shift work. The quantitative component determines the relationship between the length of shift work and labor efficiency. The results of the research show that shift work has the potential to be both beneficial and detrimental to the productivity of construction labor. Small amounts of well-organized shift work can serve as a very effective response to schedule compression. The productivity loss, obtained from the quantification model developed through this study, ranges from ?11 to 17% depending on the amount of shift work used.     

Benchmarking Productivity Indicators for Electrical/Mechanical Projects

Labor-intensive industries such as the electrical and mechanical trades are considered high risk due to the high percentage of labor costs. Because of this high risk, it is important for contractors in these industries to closely track labor costs on projects and compare these costs to industry benchmarks. In this paper, benchmark indicators for these industries are established on the basis of actual project data. These benchmarks include the relationship between the percent complete or percent time and cumulative work hours or cost, project size and duration, project size and average man power, project size and peak man power, and average versus peak man power. These relationships were developed using regression analysis. Man power loading charts and the related S-curves were developed from actual project data. The man power loading charts and the related S-curves are useful for resource planning and for tracking progress on a construction project. They can be used to show the cause-and-effect relationship between projects impacted by outside factors and normal labor productivity.     

Labor Productivity Drivers and Opportunities in the Construction Industry

The study of labor productivity in the construction industry is gaining increasing attention as the industry faces multiple problems related to its workforce. This paper presents the results of a survey instrument applied to determine the relative level of relevance of construction labor productivity drivers and opportunities. Owners, general contractors, electrical contractors, mechanical contractors, consultants, and others participated in this survey. Management skills and manpower issues were identified as the two areas with the greatest potential to affect productivity according to survey respondents. Surprisingly, external factors, which are often cited as a major cause for reduced productivity in the construction industry, were considered to be one of the least relevant productivity drivers. These results suggest that respondents consider the improvement of labor productivity within their reach and control rather than determined by external conditions.     

Impact of Change Orders on Small Labor-Intensive Projects

In today’s construction, small projects can be just as important if not more important than the larger projects. However, small projects are usually fast track projects, which often involve overlapping design and construction time. Subsequent modifications may be required for the sections that are already under construction. These disruptions to the ongoing project are labeled as change orders. The impact due to changes has been described as the adverse effect upon the unchanged work due to changes in the contract. For this study, 34 projects were selected to develop a statistical model that estimates the amount of labor efficiency lost due to change orders for small projects. The variables in the final model are percent design related changes, percent owner initiated changes, the ratio of actual peak labor to estimated peak labor, the ratio of actual project duration to estimated project duration, and project manager’s percent time on the project. The results of this paper are of value to owners, electrical and mechanical contractors, and construction managers. The model quantifies the impact of change orders by introducing the most important variables that bring the largest disruptions.     

International Labor Productivity Factors

The construction industry is increasingly involved with international projects requiring the services of nonskilled and skilled workers throughout the world. Data concerning the productivity of these workers, therefore, is a critical need for the industry. The purpose of this article is to present national labor productivity factors for 47 various countries in Central and South America, Europe, the Middle East, and Asia. In addition, specific variables affecting national and international productivity are reviewed.     

Baseline Determination in Construction Labor Productivity-Loss Claims

Various ways of quantifying damages have been applied to productivity loss claims in construction. All of the ways attempt to be as objective as possible based on the extent of information available in a particular case. The measured mile, a widely accepted method, is employed when an unimpacted baseline period of production can be identified. Although that approach is considered to be the most objective method available in such cases, the method is limited and does not directly account for variation in individual productivity values about a normal or natural level of productivity. A gap exists between the use of existing methods and the availability of an appropriate methodology that specifically addresses variation in productivity. The key lies in the way baseline productivity is measured, which is inherently statistical, yet no truly statistical methods are used to establish such a baseline. Using the measured mile as a backdrop, this article provides an objective, measurement-based approach that can be used to establish a productivity baseline applied to construction productivity loss claims, based on the application of statistical methods aided by a process control chart. The focus is on providing the basic principles and concepts underlying the approach presented.     

Using Agent-Based Modeling to Study Construction Labor Productivity as an Emergent Property of Individual and Crew Interactions

Lean construction research has shown that managing work flow effectively and maintaining labor flow on site can improve construction labor performance. Related research also shows that congestion on construction sites often leads to lowered efficiency. Using these findings as a point of departure, we use the agent-based modeling method to represent the construction site as a system of complex interactions and explore whether labor efficiency can be treated as an emergent property resulting from individual and crew interactions in space. This allows us to use a “bottom-up” approach to analyzing labor efficiency, which supplements existing “top-down” approaches to modeling the impacts of space congestion on labor efficiency. A pilot implementation of the agent-based model, and preliminary results illustrating the relationships between congestion and labor efficiency are presented. The empirical studies exhibit system behavior that support published principles of work-force management. The primary contribution of this paper is that it provides a method that can be used to efficiently utilize construction space, and develop plans and schedules that account for congestion arising from crew interactions in space.     

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.     

Is Construction Labor Productivity Really Declining?

Macroeconomics data suggest that labor productivity declined significantly in the construction industry during the 1979–1998 period. However, microeconomic studies indicate the contrary. This paper critically examines the construction labor productivity macroeconomic data in the United States from 1979 to 1998 to determine their validity and reliability. Data collection, distribution, manipulation, analysis, and interpretation are reviewed and problems are identified. The paper also presents a comparison of construction and manufacturing labor productivity during this period. The main conclusion of the study is that the raw data used to calculate construction productivity values at the macroeconomic level and their further manipulation and interpretation present so many problems that the results should be deemed unreliable. The uncertainty generated in the process of computing these values is such that it cannot be determined if labor productivity has actually increased, decreased, or remained constant in the construction industry for the 1979–1998 period.     

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.     

On Site Performance Improvement Programs

The paper presents the various phases in a comprehensive On Site Performance Improvement Program (OSPIP) of a medium size construction project which includes: problem identification; data collection; data analysis and planning the change content; planning the change process; and measuring and evaluating the results. Methods, techniques, and means to put the various program phases into practice are examined, and the problems likely to be encountered are discussed. An OSPIP case study of a medium size construction project is described in detail. It deals with production rate, safety, and manpower turnover problems, improvement plans to overcome them, and how implementation was realized in each of the problem areas. The paper suggests that OSPIP in medium size projects is not only economically feasible and worthwhile, but highly recommendable.     

Decision Support System for Microtunneling Applications

Microtunneling is a trenchless technology method used for installing new pipelines. The inherent advantages of this method over open-cut trenching have led to its increasing use since its first introduction into North America in the early 1980s. With this technology, surface disruption can be minimized, especially in urban areas, and high accuracy of installation (usually less than 2?cm over 100?m) can be achieved in both line and grade. But microtunneling machines are very expensive and few contractors have extensive experience with this technology. Microtunneling can also be risky when unexpected obstacles or soil changes occur. Careful constructability analysis is needed, and an appropriate microtunneling method should be selected in order to achieve successful completion of microtunneling projects. A computerized decision support system (DSS) for microtunneling was developed to support decision making for contractors who want to bid on microtunneling projects. This paper discusses the decision-making process for microtunneling and the development of the DSS. When the user enters basic information about the potential project such as drive length, installation depth, pipe diameter, and soil condition, the DSS evaluates whether microtunneling will be economically feasible and suggests appropriate types of microtunneling methods. The user can then select microtunneling machines, types of pipes, and types of shaft construction methods. This DSS is most beneficial when used at the preplanning stage by utility contractors.     

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.