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.     

Statistical-Fuzzy Approach to Quantify Cumulative Impact of Change Orders

This paper presents a hybrid approach to quantify the impact of change orders on construction projects using statistical regression and fuzzy logic. There are many qualitative variables affecting the impact of change orders on labor productivity; statistical analysis falls short of addressing the fuzziness of those variables. Because of their complementary nature, fuzzy logic and regression analysis can be integrated; regression analysis is used to determine the membership functions of the input linguistic values. In this paper, each input variable is statistically treated before entering a general rule relating its space to the space of loss in labor productivity. The relative weight of each input variable is determined by its coefficient of determination (R2) value. The expected loss of labor productivity and its standard deviation are then determined from the output fuzzy membership function. The proposed methodology is general and can be applied in areas of system analysis and decision making when a complex input-output function is to be predicted in the presence of some fuzzy knowledge and a large number of real input-output data.     

Model for Quantifying the Impact of Change Orders on Project Cost for U.S. Roadwork Construction

Change orders are very common in almost every construction project nowadays, often resulting in increases of 5–10% in the contract price. Understanding the consequences of such trends, several studies have attempted to quantify the impact of change orders on the project cost. Most of the studies aimed at the quantification of the change orders were sponsored by contractors’ organizations, where statistical models used to quantify the impact of the change orders on the project cost were based on data supplied by the contractors; a situation that can lead to owner-contractor disagreements related to the quantification method used. In addition, most of the studies tackled commercial and electromechanical work, and very rare studies tackled the field of heavy construction; a field that suffers from change orders because of errors and omissions, scope of work changes, or changes because of unforeseen conditions. This study addresses the need for a statistical model to quantify the increase of the contract price due to change orders in heavy construction projects in Florida. The model is based on data collected from 16 Florida DOT projects with contract values that ranged between $10–$25 million, and that encountered an increase in the contract price from 0.01 to 15%. Eleven variables were analyzed to test their impact on the cost of the change orders. The study concluded that most significant variables that impact the value of the change order, which are (1) the timing of the change order and (2) when the reason for issuing the change order is unforeseen conditions. Two regression models are developed and validated as follows: (1) a model to quantify the percentage increase in the contract price due to the change orders that increase the contract price from 0.01 to 5% and (2) a model to quantify the percentage increase in the contract price due to the change orders that increase the contract price from 5 to 15%. Those models will provide the owner with a retrospective or forward pricing of the change orders, and hence, allow the owner to estimate and utilize contingency amounts.     

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.     

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.     

Quantitative Definition of Projects Impacted by Change Orders

Change, defined as any event that results in a modification of the original scope, execution time, or cost of work, is inevitable on most construction projects due to the uniqueness of each project and the limited resources of time and money available for planning. Change may occur on a project for a number of reasons, such as design errors, design changes, additions to the scope, or unknown conditions. For each change, contractors are entitled to an equitable adjustment to the base contract price and schedule for all productivity impacts associated with the change. Changes may or may not have an impact on labor productivity. Existing literature uses subjective evaluation to determine whether the project is impacted. Projects impacted by change cause the contractor to achieve a lower productivity level than planned. The focus of this paper is to quantify whether an electrical or mechanical project is impacted by a change order. Through statistical hypothesis testing, groups of factors that correlate with whether a project is impacted by change orders were identified and used to develop a quantitative definition of impact. Logistic regression techniques were used to develop models that predict the probability of a project being impacted. The results of this research show that percent change, type of trade, estimated and actual peak manpower, processing time of change, overtime, overmanning, and percent change related to design issues are the main factors contributing to the project impact.     

Reconciliation of Owner and Contractor Views in Heavy Construction Projects

Delay and loss of productivity are the two main types of damage experienced by the contractor when the owner issues a change order. Courts have recognized critical path method schedule analysis as the preferred method of identifying and quantifying critical delays. As for the inefficiency damages, there is no direct way of measuring inefficiency due to its qualitative nature and the difficulty of linking the cause of the productivity loss to the damage. Most of the scholarly work published in this area was based on data supplied by the contractors; and that explains why there are discrepancies between what the contractor asks for and what the owner believes the contractor is entitled to. This study addresses the need for a statistical model to quantify the productivity loss from verifiable site data such as owner’s daily reports, change orders, drawings, and specifications, rather than rely solely on contractor surveys. A model is developed and validated to quantify the productivity loss in pipe work in roadway projects due to the change orders. The productivity loss study analyzed two sets of data that include: (1) variables that predict which of the two parties, the owner and the contractor, contributed to the productivity loss; and (2) variables that predict, from the legal viewpoint, productivity losses which only the owner is responsible for. The study showed the difference between what the contractor asked for and what he/she is actually entitled to. This model can be used by both the owner and the contractor to quantify the productivity loss due to change orders, and to offer an objective approach to reconcile their differences. This study concludes with an example to demonstrate the use of the model.     

Effects of Delivery Systems on Change Order Size and Frequency in Mechanical Construction

Change orders represent one of the largest sources of cost growth on building construction projects. Field generated, or “unforeseen” change orders can also be highly disruptive to field productivity. Design-build delivery methods can potentially help minimize change orders on construction projects. This study was performed to closely examine the effects of delivery methods on the frequency and magnitude of change orders in mechanical construction, and how design-build business practices can be used to minimize the frequency of field generated chance orders. In a study of 598 change orders occurring on 120 construction projects performed by the same contractor, the total number of change orders was found to be close to the same on design-build and design-bid-build projects, however an 87% decrease in the average number of unforeseen change orders was observed on design-build projects versus design-bid-build projects. In addition, the average size of unforeseen change orders was 86% smaller on design-build projects. A detailed and qualified presentation of the research methodology and resulting data is provided. Key attributes and business practices leading to the results are discussed and practical applications of this research for owners and contractors are provided.     

Productivity and Cost Regression Models for Pile Construction

The estimating process of pile construction productivity and cost is intricated because of several factors: unseen subsurface obstacles; lack of contractor experience; site planning; and pile equipment maintainability. This study intends to assess cycle time, productivity, and cost for pile construction considering the effect of the above factors using regression technique. Data were collected through designated questionnaires, site interviews, and telephone calls to experts in different construction companies. Many variables have been considered in the pile construction process. Seven regression linear models have been designed and validated to assess productivity, cycle time, and cost. Consequently, three sets of charts have been developed based upon the validated models to provide the decision maker with a solid planning, scheduling, and control tool for pile construction projects. This research is relevant to both industry practitioners and researchers. It provides sets of charts and models for practitioners’ usage to schedule and price out pile construction projects. In addition, it provides the researchers with the methodology of designating regression models for the pile construction process, its limitations, and future suggestions.     

Change Orders Impact on Labor Productivity

This paper describes a study conducted to investigate the impact of change orders on construction productivity and introduces a new neural network model for quantifying this impact. The study is based on a comprehensive literature review and a field investigation of projects constructed in Canada and the USA. The field investigation was carried out over a 6-month period and encompassed 33 actual cases of work packages and contracts. Factors contributing to the adverse effects of change orders on labor productivity are identified and a model presented earlier is expanded to account primarily for the timing of change orders, among other factors. The developed model, as well as four models developed by others, have been incorporated in a prototype software system to estimate the loss of labor productivity due to change orders. A numerical example is presented to demonstrate the use of the developed model, and illustrate its capabilities.     

Impact of Change Orders on Labor Efficiency for Mechanical Construction

Change orders have become an everyday occurrence in construction. It is widely accepted by both owners and contractors that change orders have an effect on the labor efficiency, but these effects are difficult to quantify and frequently lead to disputes. Data from 61 mechanical construction projects were collected to develop a statistical model that estimates the actual amount of labor efficiency lost due to the change orders. The input variables needed in the model are as follows: (1) The original estimated labor hours; (2) impact classification; (3) total estimated change hours; (4) number of change orders; and (5) the timing of changes. The results of this study show that impacted projects have a larger decrease in labor efficiency than unimpacted projects. Additionally, the later a change order occurs in the life of a project the more impact it will have on the labor efficiency. The results appear to be consistent with the intuition of experienced professionals. Although each project has unique characteristics, the resultant model provides owners and contractors with a baseline measure of lost labor efficiency.     

Contributors to Construction Debris from Electrical and Mechanical Work in Hong Kong Infrastructure Projects

The crucial problem of construction debris is of increasing concern in Hong Kong. In the construction industry, the electrical and mechanical (E&M) installations in the infrastructure, for example, buildings, tunnels, or dams, are some of the major and usually complex components. Difficulty in coordinating the various trades affects productivity in general, and has a major impact on the quantity of construction debris. By identifying the sources of waste at each stage of E&M engineering work, some of the construction debris can be eliminated at the source during production. This paper investigates the critical production shortcomings in the E&M sector in Hong Kong. The study is based on a survey that includes a preliminary questionnaire survey, brainstorming exercises with a focus group, structured interviews with experienced frontline supervisors, and a second focus group exercise to test findings and proposed measures. The principal findings are that “poor coordination” and “design changes and/or errors” are major contributors to variations or change orders and rework, which in turn result in a high volume of construction debris. The results also indicate that construction debris can be minimized in the E&M sector of the construction industry, if the material wastes from incidental work are reduced and also controlled better in a new work process flow pattern through recommended construction project management improvements for reducing critical production shortcomings.     

Case Law and Variations in Cumulative Impact Productivity Claims

Proving and quantifying lost productivity due to cumulative impacts of multiple changes are difficult tasks. This paper presents the most acceptable methods from case law and demonstrates their applications for analyzing the loss of productivity. These methods include earned value analysis, measured mile analysis, and combinations of these two. They are either well established or drawn from recent court and board decisions. A case study is used to illustrate and compare the use of these methods. These methods result in considerably different loss of productivity values though the actual amount (i.e., inefficiency in labor hours) is unique for a particular case and though these methods are often thought to be similar or even the same. How a measured mile analysis and its variants are employed affects the amount of lost productivity estimated. The variants can avoid some drawbacks of measured mile and earned value studies. Nevertheless, which method is more accurate and reliable is difficult to provide for a particular claim. Practitioners should choose between them based on the availability of project records and the nature of changes and cumulative impacts. Practitioners may also employ two or more methods to perform a “sensitivity analysis” of the chosen methods and persuade the other party and/or the jury that their estimate of lost productivity is sufficiently certain.     

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.     

Logistic Likelihood Analysis of Mediation Outcomes

Dispute persists in all building and construction projects. Alternative dispute resolution methods are now commonly used as a means to resolve construction disputes. Mediation, interalia, is the popular choice in Hong Kong due to its cost-saving, flexible, speedy, confidential, and voluntary attributes. In mediation, tactics used by a mediator is central in driving desired outcomes. This paper reports a study that employs logistic regression (LR) to predict mediation outcomes respective to the tactics used. To achieve this, three main stages of work are involved. First, taxonomies of mediator tactics and mediation outcomes were developed. With these, the second stage included the development of logistic regression models each with a mediation outcome taxonomy as dependent variables and the taxonomies of tactics as independent variables. In the third stage of the study, the LR models were validated using an independent set of testing data. The LR models suggested that “win-win settlement,” “progress,” “improvement,” and “time advantage” are responsive to mediator tactics of “ice-breaking,” “trust building,” “encourage for self-improve,” and “process control,” respectively. In addition, it is observed that these relationships are positively correlated.     

Empirical Evaluation of Structural Frame Performance Criteria: Realizing the Potential of Hybrid Concrete Construction

Despite its enormous potential to improve performance, hybrid concrete construction (HCC) is currently underutilized. To demonstrate the benefits of using HCC (sometimes referred to as “mixed” construction) within the industry, it is essential that transparent criteria to assess this structural frame type against alternatives be determined, defined, and evaluated. Following a thorough review of literature in the building performance domain, a survey of U.K. experienced practitioners including clients, engineers, architects, quantity surveyors, and main contractors was conducted to obtain their perceptions regarding the importance of structural frame performance criteria (SFPC). A factor analysis of SFPC revealed seven dimensions, interpreted as “physical form and space,” “construction process,” “long-term sustainability,” “establishing confidence,” “building impact,” “physical appearance,” and “client satisfaction.” These dimensions should improve the decision making process when selecting an appropriate structural frame during early project stages by providing a simple list of performance criteria to be considered. Although these SFPC and dimensions were originally developed to ensure that the benefits of using HCC were apparent during the frame selection process, they are equally applicable to all frame choice comparisons. As such, they may provide a valuable tool for ensuring added value and client satisfaction.     

Integrated Simulation System for Construction Operation and Project Scheduling

Simulation modeling is important in predicting the productivity of construction operations and the performance of project schedules. It would be desirable if operation and project models are vertically integrated in practice. However, existing discrete event simulation systems do not allow integrating operation and project models. This paper introduces an integrated simulation system named “Construction Operation and Project Scheduling” (COPS). COPS analyzes the productivity of construction operations as well as the performance of a project schedule individually and jointly. It creates operation models, maintains these models in its operation model library, conducts sensitivity analysis with different resource combinations, finds the optimal resource combination that satisfies the user’s requirements relative to hourly production and hourly cost of the operation, feeds this information into a project schedule, and executes stochastic simulation-based scheduling. A case study is presented to demonstrate this integrated simulation system.     

Exploring Factors for Ethical Decision Making: Views from Construction Professionals

The construction industry has experienced malpractice, which is a significant cause for concern. Does the nature of the industry provide some hint for tracing the underlying reasons of the relatively severe ethical problems in construction? Can one of the most influential stakeholders in the industry, the professionals, improve the situation? This study was conducted in Hong Kong to investigate what kinds of ethical problems are faced by construction professionals, and to explore the factors influencing their ethical decision making. Statistical factor analyses identified six intuitive underlying factors that affect ethical decision making when construction professionals are facing ethical dilemmas in the industry. Four of them influencing decision making are “deontological factor,” “consequential factor—egoism,” “consequential factor—utilitarianism,” and “legal requirement.” Two other factors are “oneself/family” and “stakeholders.” Results show that construction professionals’ ethical behavior is guided by legal requirements. When making ethical decisions, they adopt an egoism approach that prioritizes their self-interest and their families’ interests higher than the public’s interest; they would consciously evaluate the consequences of any unethical behavior.     

Productivity and Cost Assessment for Continuous Flight Auger Piles

Continuous flight auger (CFA) piles do not receive sufficient research attention although they are widely used in the United States, United Kingdom, and other countries in the world. Therefore, this paper focuses on describing the CFA pile installation features, exploring its construction methods, and determining the factors that affect the CFA pile productivity and cost. A productivity index (PI) is developed to quantify the effect of subjective factors on the CFA process productivity. Current research concludes a PI value of 0.78 out of 1.0 (efficiency). Several models are designated to assess the CFA pile’s cycle time, productivity, and cost using deterministic approach. Various charts are developed to show this assessment based upon these models. Results show that the cycle time for 12.19?m depth and (0.36?m) (14?in) diameter piles is 16?min; however, its productivity is 24 piles (holes)/day. The developed tools play an essential role in the CFA decision making process. This paper is relevant to both researchers and practitioners. It provides charts for practitioners’ usage to schedule and price out the CFA pile construction projects. In addition, it provides the researchers with a methodology of applying the deterministic approach to the CFA pile construction process and its limitations.     

Competency-Based Model for Predicting Construction Project Managers’ Performance

Using behavioral competencies to influence human resource management decisions is gaining popularity in business organizations. This study identifies the core competencies associated with the construction management role and, further, develops a predictive model to inform human resource selection and development decisions within large construction organizations. A range of construction managers took part in behavioral event interviews where staff were asked to recount critical management incidents, decisions, and actions from which their key competencies could be identified. By delineating the sample according to their levels of performance measured against a range of role-specific performance criteria, the competencies defining superior management performance could be determined. These were then used to construct a logistic regression model from which a project manager’s performance can be predicated. The validated results reveal that “self-control” and “team leadership” are the most predictive behaviors of effective project management performance within the framework of the model. The paper explores the potential role and application of the framework to underpin human resource management decision making with regards to recruitment, performance management, succession planning, and resource allocation.