Improved Measured Mile Analysis Technique

Quantifying lost labor productivity on construction projects is difficult and sometimes subjective. A widely accepted way to quantify losses is the “measured mile” approach. It compares periods of a project that have been impacted by change to those that have not been impacted. As currently practiced the measured mile relies on subjectively identifying that reference period. In this paper the measured mile and a variant, the baseline method, are analyzed and compared to a new, proposed statistical clustering method. This new approach is advocated because it determines its reference period using objective criteria. A case study is included to show how the three methods work, and advantages and disadvantages of each method are presented in this paper.     

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.     

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.     

Method for Calculating Schedule Delay Considering Lost Productivity

A delay claim often occurs when a difference between the actual completion date and the contract completion date exists. The duration of a delay is an essential piece of information required for determining the cause of a delay. However, it is difficult to analyze a delay claim due to the fact that numerous factors that cause this delay, thereby making it a very complex issue. One of such factors is the lost productivity or loss of productivity. Despite the fact that it is one of the major causes of delay, there have been only a few studies that focus on converting lost productivity into delay duration carried out to date. Claims for productivity losses are generally the result of tension between the contractor and the owner. This tension arises due to the great difficulty involved in quantifying disruption effects. Thus, to calculate accurately the delay duration, a logical method for analyzing schedule delay caused by lost productivity is necessary. Therefore, in this study, we propose a method for analyzing construction schedule delay where this lost productivity is taken into consideration. This methodology was implemented on a case project to ascertain its practicability, and to decide whether it can be utilized in the case of a delay claim related to lost productivity. The significance of this paper is twofold. One is the method to convert the lost productivity into the delay duration, which can be applied to reasonable delay claim settlement. The other is the process to analyze the construction schedule delay considering lost productivity.     

Quantification of Losses of Labor Efficiencies: Innovations in and Improvements to the Measured Mile

This paper describes a rigorous procedure for quantifying damages arising from loss of labor inefficiency. Throughout, deficiencies with the measured mile concept are cited. Perhaps one of the least recognized deficiencies is that the measured mile is a concept, not a procedure. Thus, every analyst is left to conduct the study as he/she pleases. This paper defines a step-by-step process for doing a damage study, and cites rules to follow when performing the study. Following the procedure and rules will lead to more rigorous and defensible analyses. A baseline analysis is described and presented as a way of adhering to the measured mile concept. The principle difference is in how best performance is defined. In a baseline study, the focus is on periods of high output (production), not good productivity. The baseline periods need not be continuous. Also, the analysis is performed on combined data (one account), not many accounts. The calculations used to perform a baseline study are illustrated using a simple case study project. Reasons for recovering damages because of losses of labor efficiency are also cited.     

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.     

Impact of Overmanning on Mechanical and Sheet Metal Labor Productivity

This paper details the impacts of overmanning on labor productivity for labor-intensive trades, namely mechanical and sheet metal contractors. Overmanning, as used in the following research, is defined as an increase of the peak number of workers of the same trade over the actual average manpower used throughout the project. The paper begins by reviewing literature on the effects of overmanning on labor productivity. Via a survey to various contractors, data were collected from 54 mechanical and sheet metal projects located across the United States. Various statistical analysis techniques are then performed to determine quantitative relationship between overmanning and labor productivity. These techniques include the stepwise method, T-test, P-value tests, analysis of variance, and multiple regression analysis. The results indicate a 0–41% loss of productivity depending on the level of overmanning and the peak project manpower. Cross-validation is performed to validate the final model. Finally, a case study is provided to demonstrate the application of the model.     

Analysis of Adverse Weather for Excusable Delays

Severe weather conditions can be disruptive to construction. Contractors typically obtain time extensions for weather days beyond normal conditions. However, contracting parties often dispute the extent of weather-related time extensions. Typical industry contracts may overlook many important points that can provide an acceptable resolution. This paper classifies seven factors causing discrepancies in analysis of adverse weather for time extensions; namely, the definition of normal weather, weather thresholds, type of work, lingering days, criteria for lost days, lost days equivalent due to lost productivity, and work days lost versus calendar days lost. An analysis of an actual weather-caused delay claim illustrates the impacts of those factors on the outcomes of the analysis. A contract should define anticipated weather delay days and their lingering days and provide threshold values for weather parameters to differentiate between predictable and unpredictable severe weather. The contract should clearly define how a time extension is granted in calendar days as a result of work days lost, and also address how a time extension is granted due to inefficiency caused by unusually severe weather. Future research may provide an appropriate mechanism for analyzing equivalent lost days to account for lost productivity.     

Incorporation of n-3 polyunsaturated fatty acids into processed foods

The aim of dose reduction of chemotherapeutic agents following weight loss is to avoid excessive toxicity while maintaining an equivalent therapeutic effect. Several methods of calculating this dose reduction are currently in use, including dose reduction in proportion to the reduction in body surface area (BSA) or the amount of weight lost and no dose reduction unless significant toxicity occurs. Each of these methods results in the administration of a different dose and therefore different drug exposure, as measured by the area under the time versus concentration curve (AUC). We have used pharmacokinetic modeling software and normative data on the pharmacokinetics of high-dose methotrexate to determine the change in AUC resulting from dose reduction by each of the methods cited for patients with weight loss. Dose reduction in proportion to the reduction in weight results in the same AUC and therefore equivalent drug exposure as before weight loss. In contrast, the more common practice of dose reduction in proportion to the decrease in BSA (as determined by recalculating BSA) results in a higher AUC than before weight loss. This results in increased drug exposure and potentially increased toxicity, which may be avoided if dose reduction is carried out in proportion to the decrease in weight rather than in BSA. The same principles are applicable to other drugs, particularly those associated with dose-dependent toxicity.     

Solid particle erosion mechanisms in copper and two copper alloys

The amount and mechanisms of metal loss has been studied for oblique and normal impact on copper, 70/30 brass, and precipitation hardened Cu-2 pct Be, using steel ball bearings and velocities from 138 to 300 m/s. The weight losses are roughly the same for the three alloys though the mecahnisms of flow and fracture change from highly plastic for copper to pronounced adiabatic shear localization for the Cu-Be alloy. At these high velocities and ball sizes the deformation is adiabatic, thus localizing the flow and enhancing loss compared to a slow isothermal indentation. Also, inertial forces are large at the base of any extruded lip, thus playing a central role in removing metal. For normal impacts the principle weight loss occurs where the impacts partially overlap, and stems from the concentration of strain at local irregularities in the surface. Formerly a Graduate Student in the Department of Metallurgical Engineering, is now a Graduate Student in The Department of Ceramic Engineering at Ohio State University, Columbus, OH 43210.     

Performance-Based Optimization of Land and Water Resources within Irrigation Schemes. II: Application

The area and water allocation model which uses simulation–optimization technique for optimum allocation of land and water resources to different crops cultivated in different allocation units of the irrigation scheme was modified to include both productivity and equity in the process of developing the allocation plans for optimum productivity and/or maximum equity. This paper illustrates the potential of this approach with the help of a case study on Nazare medium irrigation scheme in India. The allocation plans were developed for optimization of different performance parameters (productivity and equity) for different management strategies based on irrigation amount and irrigation interval and cropping distribution strategies of free and fixed cropping. The results indicated that the two performance objectives productivity and equity conflict with each other and in this case, equitable water distribution may be preferred over free water distribution at the cost of a small loss in productivity. Though these results relate to one case study, they show the value of the approach of incorporating productivity and equity in the allocation process with the help of the simulation-optimization model described in the companion paper.     

Demineralization effects of 10 percent carbamide peroxide

OBJECTIVES: The purpose of this in vitro study was to measure the amount of calcium lost from enamel exposed to a 10% carbamide peroxide solution. METHODS: Nine teeth (3 incisors, 2 premolars, 4 molars) were sectioned to serve as paired test and control specimens. Each tooth half was coated with wax, leaving a uniform 3 mm x 4 mm window of exposed enamel. Test specimens were placed in culture tubes with 1.00 ml of deionized water and 0.02 ml of 10% carbamide peroxide for 6 h; controls were exposed to water alone. Calcium concentrations in the solutions were measured using a Perkin-Elmer 5100 atomic absorption spectrophotometer. RESULTS: Teeth exposed to carbamide peroxide lost an average of 1.06 micrograms/mm2 of calcium. This amount of loss was significantly greater than controls (p < 0.001), according to ANOVA. For comparison, teeth were exposed to a cola beverage for 2.5 min, the time equivalent of drinking a 16 oz. beverage. The amount of calcium lost from these teeth was also about 1 microgram/mm2. CONCLUSIONS: Teeth exposed to 10% carbamide peroxide lost calcium. However, the amount of loss was small, and may not be clinically significant.     

Probabilistic Forecasting of Project Duration Using Kalman Filter and the Earned Value Method

The earned value method (EVM) is recognized as a viable method for evaluating and forecasting project cost performance. However, its application to schedule performance forecasting has been limited due to poor accuracy in predicting project durations. Recently, several EVM-based schedule forecasting methods were introduced. However, these are still deterministic and have large prediction errors early in the project due to small sample size. In this paper, a new forecasting method is developed based on Kalman filter and the earned schedule method. The Kalman filter forecasting method (KFFM) provides probabilistic predictions of project duration at completion and can be used from the beginning of a project without significant loss of accuracy. KFFM has been programmed in an add-in for Microsoft Excel and it can be implemented on all kinds of projects monitored by EVM or any other S-curve approach. Applications on two real projects are presented here to demonstrate the advantages of KFFM in extracting additional information from data about the status, trend, and future project schedule performance and associated risks.     

Cloning and characterisation of a thermostable alpha-DNA polymerase from the hyperthermophilic archaeon Thermococcus sp. TY

The mortality patterns of men and women of working age, in terms of the major causes of death, have changed over the past three decades. This study assesses the extent to which mortality among persons of working age represents an economic loss to society. This economic loss is measured by the per capita loss of productive working life, defined as the number of years, on the average, a person can expect to be an active member of the labor force. Causes of death affecting primarily older Americans (heart disease, cancer, stroke) had a relatively small and declining impact on the working lives of men and women. Major causes of death affecting the young (motor vehicle accidents, homicide, AIDS), although accounting for fewer deaths, were responsible for many more years of lost productivity. Gender and socioeconomic differentials in mortality suggest that different strategies are necessary for future reductions in lost work-years.     

Improving Productivity Estimates by Work Sampling

Theoretical aspects are presented to evaluate the adequacy of work sampling as a surrogate productivity measure. A 10‐week study is described in which work sampling data were gathered simultaneously with earned value information for a 10‐man, small‐bore pipefitter crew. Two forms of work sampling data were gathered that differed principally in the definition of direct work. The direct work percentages are statistically correlated to the ratio of earned to actual man‐hours, and it is shown that the Pearson product‐moment correlation coefficient could be improved by as much as 86% if the definition of direct work is restricted. The probability of a Type I statistical error is also greatly reduced. Data summarized according to 5‐ or 7‐day moving averages is found to be more reliable than daily or weekly averages. It is shown how a prediction equation could be developed. The slope of this equation suggests that rather significant changes in the earned to actual man‐hour ratio are reflected by rather small changes in direct work.     

Can profile analysis of ability test scores work? An illustration using the PASS theory and CAS with an unselected cohort.

A new approach to ipsative, or intraindividual, analysis of children's profiles on a test of ability was studied. The Planning, Attention, Simultaneous, and Successive (PASS) processes measured by the Cognitive Assessment System were used to illustrate how profile analysis could be accomplished. Three methods were used to examine the PASS profiles for a nationally representative sample of 1,597 children (aged 5–17 yrs) in both regular and special educational settings. Children with significant ipsatized PASS scores, called Relative Weaknesses (RW), were identified. Results indicated that these children earned average scores on PASS and achievement, showing that the ipsative approach did not identify a group of children with low achievement scores. In contrast, children with Cognitive Weaknesses (CW) earned lower achievement scores. CW scores were those that showed significant intraindividual variation using the ipsative approach and one of the PASS scores was below a cut score of 90, 85, or 80. Children with a CW at each level were also more likely to have been previously placed in special education settings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

FLORA: New Forensic Schedule Analysis Technique

While various factors such as float ownership, logic change, and resource allocation (FLORA) affect results of delay analysis, existing delay analysis techniques tend to ignore most if not all of them. To address this insufficiency this paper proposes a new schedule analysis technique called FLORA that simultaneously captures the dynamics of float, logic, and resource allocation in its analyses. FLORA analyzes not only the direct impact of a delay but also its “secondary” effect. The analysis process follows ten rules that are flexible and customizable. A case study is employed to illustrate its application. FLORA yields different and more reasonable outcomes compared to the window analysis technique. Each single analysis may also yield different or even conflicting results. By properly dealing with the current issues of schedule analysis, FLORA can be more reliable. Practitioners may readily accept its analyses and outcomes because they are able to specify, modify, and consent to the rules for schedule analysis to fit into a specific context in advance. Researchers may further evaluate the impacts of different factors on delay responsibility or apply FLORA to real projects to assess its strengths, weaknesses, and potential improvements.     

Quantitative Impacts of Project Change: Size Issues

Change has a tremendous effect on the performance of a construction project. Research that focuses on the quantitative impact is limited, incomplete, and in some cases questionable. The goals of this study were to quantify the nature and impacts of project change and develop recommended practices so that owners and contractors can manage change better. The focus was on project change during detailed design and construction, in particular the size of change and its impact on the project. These results show that the amount of change is negatively correlated with productivity and total installed project cost, whether within the design phase or construction phase, or between them. The greater the amount of change the more productivity and costs are degraded. Recommendations are also offered here on how to mitigate the impact of project change.     

Factors Affecting Absenteeism in Electrical Construction

Construction contractors continue to be faced with the challenge of improving productivity in order to remain successful in an increasingly competitive industry. One factor that contributes to lower productivity is absenteeism. Yet, little is known or understood about the reasons electricians miss work, and very few studies have been conducted on absenteeism in the construction industry. As a positive step to reduce the problems associated with voluntary and involuntary absences, a study was initiated to identify why workers miss work and what steps should be taken to minimize absenteeism. The main objectives of the study were to learn the reasons for absenteeism and to quantify the impacts so that solutions can be developed to help contractors improve their productivity. The study determined that managers and electricians agreed that illnesses and medical appointments were two common reasons workers missed work. However, managers also believed workers were absent because of a lack of interest or irresponsibility, while electricians reported injuries and unsafe working conditions as reasons for missing work. Furthermore, a quantitative analysis of the data revealed that when the absenteeism rate was between 0 and 5%, there was no loss in productivity. However, when the absenteeism rate was between 6 and 10%, a 24.4% loss in productivity was experienced. By understanding what causes electricians to miss work, and the effect of absences on productivity, a company can manage and control absenteeism on electrical construction projects.     

Impact during equine locomotion: techniques for measurement and analysis

Impact is implicated in the development of several types of musculoskeletal injury in the horse. Characterisation of impact experienced during strenuous exercise is an important first step towards understanding the mechanism for injury. Measurement and analysis of large, short duration impacts is difficult. The measurement system must be able to record transient peaks and high frequencies accurately. The analysis technique must be able to characterise the impact signal in time and frequency. This paper presents a measurement system and analysis technique for the characterisation of large impacts. A piezo-electric accelerometer was securely mounted on the dorsal surface of the horses hoof. Saddle mounted charge amplifiers and a 20 m coaxial cable transferred these data to a PC based logging system. Data were down-loaded onto a UNIX workstation and analysed using a proprietary statistics package. The values of parameters calculated from the time series data were comparable to those of other authors. A wavelet decomposition showed that the frequency profile of the signal changed with time. While most spectral energy was seen at impact, a significant amount of energy was contained in the signal immediately following impact. Over 99% of this energy was contained in frequencies less than 1250 Hz. The sampling rate and the frequency response of a measurement system for recording impact should be chosen carefully to prevent loss or corruption of data. Time scale analysis using a wavelet decomposition is a powerful technique which can be used to characterise impact data. The use of contour plots provides a highly visual representation of the time and frequency localisation of power during impact.