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.     

Probabilistic Monitoring of Project Performance Using SS-Curves

The purpose of this study was to develop a new concept of project control. This new concept uses stochastic S-curves (SS-Curves) as an alternative to using the deterministic S-curve technique commonly employed in professional practice. SS-curves are developed by determining the activity level variability in cost and duration. Simulation is the recommended approach for obtaining SS-curves, similar to the way that stochastic schedules are currently developed. SS-curves provide probability distributions for expected cost and duration for a given percentage of work completed. Monitoring project performance is performed by comparing the most likely budget and duration values, obtained from respective probability distributions for actual progress, with the project's actual data and cumulative cost. By using this method, an evaluation of actual project performance can be developed that appropriately considers the natural variability of construction costs and duration, rather than utilizing only one possible deterministic outcome. Given the probabilistic characteristics of SS-curves, additional benefits are presented that enable a more comprehensive project control methodology.     

Optimizing Cash Flows for Linear Schedules Modeled with Singularity Functions by Simulated Annealing

The ability of construction contractors to plan and manage cash flow is critical for their economic success. The cumulative interaction of outflows (labor, materials, and equipment costs) and inflows (progress payments less retainage) creates a profile with a complex zigzag shape. This could only be modeled by simplification, e.g., as values tabulated at discrete times; averaged S-curves without peaks; or envelopes of all possible constellations. Neither is suited for a fully integrated model that dynamically links schedules with their cash flows for optimization. Therefore, singularity functions, whose components define ranges of behavior between cutoffs, are used to flexibly yet accurately model cash flow profiles and their various payment terms. The new approach augments construction project management toward an integrated planning model and is validated with an example from the literature. Optimization with a simulated annealing algorithm shifts activity positions in a randomized but directed search for maximizing profits.     

Quality and Change Management Model for Large Scale Concurrent Design and Construction Projects

Concurrent design and construction has been lauded for streamlining projects in terms of time. However, such an approach may actually make projects more uncertain and complex than the traditional sequential design and construction process. The main sources of risk that have been identified with concurrent design and construction are iterative cycles that result from unanticipated errors and changes and their subsequent impacts on project performance. As an effort to address these detrimental impacts, a framework for quality and change management that identifies those negative iterative cycles is proposed. The proposed framework is incorporated into the system dynamics model of dynamic planning and control methodology (DPM), which has been developed to evaluate negative impacts of errors and changes on construction performance. Relevant to practitioners and researchers, the potential of DPM as a robust design and construction planning methodology that could effectively deal with errors and changes inherent in the design and construction process is demonstrated through a case study involving the Treble Cove road bridge in Massachusetts.     

Dynamic Prediction of Project Success Using Artificial Intelligence

The purpose of construction management is to successfully accomplish projects, which requires a continuous monitoring and control procedure. To dynamically predict project success, this research proposes an evolutionary project success prediction model (EPSPM). The model is developed based on a hybrid approach that fuses genetic algorithms (GAs), fuzzy logic (FL), and neural networks (NNs). In EPSPM, GAs are primarily used for optimization, FL for approximate reasoning, and NNs for input-output mapping. Furthermore, the model integrates the process of continuous assessment of project performance to dynamically select factors that influence project success. The validation results show that the proposed EPSPM, driven by a hybrid artificial intelligence technique, could be used as an intelligent decision support system, for project managers, to control projects in a real time base.     

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.     

Accuracy of Highway Contractor’s Schedules

Contractors are required by the Michigan Department of Transportation (MDOT) to submit a progress schedule identifying the controlling path of activities for a construction project. During the 2000 construction season, MDOT allowed contractors to submit a progress schedule with overlapping or concurrent controlling operations. Prior to this, only one activity at a time could be controlling on the progress schedule. This paper reports on the results of a research project where the focus was to examine the accuracy of the progress schedules, which only list controlling items. Eight construction projects were studied and a determination of progress schedule accuracy was made. This was done to determine if there was an increase in accuracy of the schedules when concurrent controlling operations were used. Included in the eight projects were four without concurrent controlling activities and four with concurrent controlling activities. A comparison based upon similar projects with and without concurrent activities was made. Additionally, 22 projects were analyzed, all without concurrent controlling activities, to determine the accuracy of progress schedules for two types of projects. The comparison revealed that, in three of the four cases, the accuracy of progress schedules increased with the allowance of concurrent controlling activities. The 22 projects revealed that the accuracy of progress schedules varied considerably. It was also determined that contractors overestimated the duration of activities included in progress schedules.     

Integrated Schedule and Cost Model for Repetitive Construction Process

Several efforts have been made by many researchers to develop a model for schedule and cost integration in construction projects, but it is difficult to integrate and manage schedule and cost in an actual construction site using such a model. The integrated schedule and cost model developed in this study (1) enables the planning and control of repetitive construction processes and (2) can be used by a project manager in an actual construction site. Furthermore, an integrated schedule and cost model for the core wall construction, which is an important repetitive process in the recently booming high-rise building construction in terms of scheduling, was developed using the integration model developed in this study. It is expected that the integrated schedule and cost model developed can allow project managers to integrate the schedule and cost of repetitive construction processes more effectively and support the project managers’ decision-making.     

Effect of Preconstruction Planning Effort on Sheet Metal Project Performance

The complexity of construction industry requires the identification of work tasks and the coordination of interactions among them. As a result, construction planning is considered to be one of the most critical steps toward success and is the main focus of past research. Consequently, little research has been performed regarding the preconstruction planning, which is the planning completed by the contractor in the period between project award and project execution. This paper focuses on sheet metal preconstruction planning, primarily that of mechanical and heating ventilations and air conditioning contractors. The research was completed in three phases: phase one gathered data on the current state of preconstruction planning, phase two developed a model sheet metal preconstruction planning process to be used by sheet metal contractors, and phase three validated the model preconstruction planning process. Based on project data collected for this research, projects that used a planning process similar to the model process performed more successfully—they achieved an average profit margin of 23% while projects that were poorly planned experienced an average profit margin of ?3%.     

Statistical Analysis on the Cost and Duration of Public Building Projects

A probabilistic model is proposed to predict the risk effects on time and cost of public building projects. The research goal is to utilize a real history data in estimating project cost and duration. The model results can be used to adjust floats and budgets of the planning schedule before project commencement. Statistical regression models and sample tests are developed using real data of 113 public projects. The model outputs can be used by project managers in the planning phase to validate the schedule critical path time and project budget. The comparison of means analysis for project cost and time performance indicated that the sample projects tend to finish over budget and almost on schedule. Regression models were developed to model project cost and time. The regression analysis showed that the project budgeted cost and planned project duration provide a good basis for estimating the cost and duration. The regression model results were validated by estimating the prediction error in percent and through conducting out-of-sample tests. In conclusion, the models were validated at a probability of 95%, at which the proposed models predict the project cost and duration at an error margin of ±0.035% of the actual cost and time.     

Time-Cost Optimization of Construction Projects with Generalized Activity Constraints

Time-cost analysis is an important element of project scheduling, especially for lengthy and costly construction projects, as it evaluates alternative schedules and establishes an optimum one considering any project completion deadline. Existing methods for time-cost analysis have not adequately considered typical activity and project characteristics, such as generalized precedence relationships between activities, external time constraints, activity planning constraints, and bonuses/penalties for early/delayed project completion that would provide a more realistic representation of actual construction projects. The present work aims to incorporate such characteristics in the analysis and has developed two solution methods, an exact and an approximate one. The exact method utilizes a linear/integer programming model to provide the optimal project time-cost curve and the minimum cost schedule considering all activity time-cost alternatives together. The approximate method performs a progressive project length reduction providing a near-optimal project time-cost curve but it is faster than the exact method as it examines only certain activities at each stage. In addition, it can be easily incorporated in project scheduling software. Evaluation results indicate that both methods can effectively simulate the structure of construction projects, and their application is expected to provide time and cost savings.     

Reliability Buffering for Construction Projects

Buffering is a common practice in project planning. Project managers or schedulers have used a time contingency to guarantee the completion time of either an activity or a project. This traditional buffering, however, often fails to protect the project schedule performance, resulting in an unnecessary resource idle time. To deal with this problem, reliability buffering, a simulation-based buffering strategy, is presented. Reliability buffering aims to generate a robust construction plan that protects against uncertainties by reducing the potential impact of construction changes. The effectiveness of reliability buffering is examined by simulating a dynamic project model that integrates the simulation approach with the network scheduling approach. The research results indicate that reliability buffering can help achieve a shorter project duration without driving up costs by pooling, resizing, relocating, and recharacterizing contingency buffers. A case study of bridge construction projects also demonstrates how construction projects can benefit from reliability buffering in real world settings. Although further validation is needed, reliability buffering can potentially impact the planning and control of construction projects by improving the consideration of construction feedbacks and characteristics in buffering, and serving as an input to a dynamic project model.     

Selection Model for Delivery Methods for Multifamily-Housing Construction Projects

Selecting an appropriate delivery method that will achieve a project’s objectives and characteristics is one of the most critical factors for the project’s success. A selection model for this study was developed by using actual construction case data in quantitative data analysis methods such as logistic regression, factor analysis, and correlation analysis. The model was developed on the basis of the design-build and design-bid-build methods from various project delivery methods. To validate the developed model, comparative tests were conducted on the selection of the delivery method for multifamily-housing construction projects, which showed that the model resulted in 95.0% accuracy. It is expected that the developed selection model will enable owners to select delivery methods that accurately meet their needs characteristics, project characteristics, and external environments.     

Modeling Weather-Sensitive Construction Activity Using Simulation

This paper proposes a framework for simulating weather-sensitive construction projects that are executed under extreme weather conditions. It applies the framework steps to enable simulating and planning pipeline construction activities under severe cold weather conditions. The uncertainties caused by weather, such as extreme cold, heat, wind, or precipitation, can significantly affect a project’s schedule and produce significant deviations from the baseline schedule. The proposed framework structures a project in the way an engineer would approach it, setting out a breakdown of work activities to quantify weather effects and account for their impact on the project baseline. The proposed weather-sensitive construction simulation framework is employed to determine the effects of weather on the construction process of high-density polyethylene (HDPE) pipe installation. The relevant simulation findings are reported to clarify the impact of extreme weather events on construction projects and to assist in project planning and decision support.     

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.     

Construction Planning Method Using Case-Based Reasoning (CONPLA-CBR)

During the construction process, there occur many unexpected events that hinder timely completion of a project. One plausible solution in formulating a robust plan against such uncertainties is to provide the proper construction duration by utilizing as-built schedules in which past events are stored for similar future projects. Many schedulers thus develop schedules based on similar past schedules, taking into consideration the dynamic construction environment. As a result, construction schedulers normally refer to similar past schedules for their current projects. Few studies on the reuse of past schedules using case-based reasoning (CBR) have been conducted, and those that are available are limited to specific areas of construction such as apartment-building construction and boiler manufacturing. This research has an emphasis on developing a CBR-based general planning tool with higher applicability, which consists of generic attributes with the capability to be customized to the given project. To address this issue, construction planning by CBR (CONPLA-CBR) is presented as a generic planning tool for various types of construction projects. CONPLA-CBR, with the dynamic case approach and construction schedule data mart, also developed in this research, helps schedulers to utilize past schedules. CONPLA-CBR was not only verified to be of practical utility by experts, but also, because it uses past cases to which the successor relationships pertain, it does not require users to input all relationships. Whereas the proposed CONPLA-CBR generates master schedules at the preconstruction stage, its concept can also be applied to the construction stages to generate more detailed, for example, weekly or monthly, schedules. Thereby, CONPLA-CBR would enhance construction performance through the increased application of CBR in construction.     

Probabilistic Duration Estimation Model for High-Rise Structural Work

The duration of a construction project is a key factor to consider before starting a new project, as it can determine project success or failure. Despite the high level of uncertainty and risk involved in construction, current construction planning relies on traditional deterministic scheduling methods that cannot clearly ascertain the level of uncertainty involved in a project. This, subsequently, can prolong a project’s duration, particularly when that project is high-rise structural work, which is not yet a common project type in Korea. Indeed, among construction processes, structural work is notable, as it is basically performed outdoors. Thus, no matter how precisely a schedule is developed, such projects can easily fail due to unexpected events that are beyond the planner’s control, such as changes in weather conditions. Therefore, in this study, to cope with the uncertainties involved in high-rise building projects, a probabilistic duration estimation model is developed in which both weather conditions and work cycle time for unit work are considered to predict structural work duration. According to the proposed estimation model, weather variables are divided into two types: weather conditions that result in nonworking days and weather conditions that result in work productivity rate (WPR) change. Obtained from actual previous data, the WPR is used with relevant nonworking day weather conditions to modify the actual number of working days per calendar days. Furthermore, on the basis of previous research results, the cycle time of the unit work area is assumed to follow the β probability distribution function. Thus, the probabilistic duration model is valid for 95% probability. Finally, a case study is conducted that confirms the model can be practically used to estimate more reliable and applicable probabilistic durations of structural work. Indeed, this model can assist schedulers and site workers by alerting them, at the beginning of a project, to project uncertainties that specifically pertain to structural work and the weather. Thus, the proposed model can enable personnel to easily amend, and increase the reliability of, the construction schedule at hand.     

Optimal Allocation of Construction Planning Resources

Efficient allocation of resources for construction planning activities requires construction planning resource requirements to be determined on a cost-effective and value-adding basis. However, although some research studies have indicated that increasing resource allocations to construction planning activities will lead to improved project performance, other research studies have indicated that investing in construction planning beyond an optimum point will lead to a deterioration in project performance. This study explored the concept of optimal planning of construction projects by examining 52 building projects undertaken in Australia. The relationships between planning input (ratio of planning costs to total project costs) and the probabilities of achieving poor performance and good performance were modeled using logistic, linear, and curvilinear regression analyses. A probable optimum planning input based on the sample studied was derived. It is suggested that any additional planning efforts beyond this optimum point would be essentially wasted because the additional planning costs would not achieve any savings in project cost but merely add to the overhead costs and therefore increase the overall project cost. A model for optimal planning is presented and discussed.     

Construction Engineering—Reinvigorating the Discipline

Construction engineering is all about production, and producing something useful is the very reason for projects to exist. How then to explain why construction engineering has progressively fallen out of focus in construction project management education and research? For an answer, the development of the discipline of construction management since the 1950s must be understood, a development that yielded a non-production-oriented approach to project management, one that provides the currently accepted operating system for managing the work in projects. This paper first traces the history of the development of the traditional operating system and related commercial terms and organizational practices. It argues that traditional practices rest on an assumption that careful development of a project schedule, managing the critical path, and maximizing productivity within each activity will optimize project delivery in terms of cost and duration. Subsequently, an alternative operating system, developed and proposed by the Lean Construction community, is described. In contrast to the traditional approach, lean defers detailed planning until closer to the point of action, involves those who are to do the work in designing the production system and planning how to do it, aims to maximize project performance (not the pieces), and exploits breakdowns as opportunities for learning. The history of this development will be traced in broad strokes.     

Predicting Accuracy of Early Cost Estimates Based on Estimate Quality

The accuracy of an estimate is measured by how well the estimated cost compares to the actual total installed cost. The accuracy of an early estimate depends on four determinants: (1) who was involved in preparing the estimate; (2) how the estimate was prepared; (3) what was known about the project; and (4) other factors considered while preparing the estimate. This paper presents results of a research effort that developed an estimate scoring system to measure the impact of these four determinants on estimate accuracy. The estimate scoring system consists of 45 elements and is organized into 4 divisions. Data were collected from 67 projects, representing $5.6 billion in total installed costs, and used to correlate the estimate scores with estimated versus actual costs. Statistical analyses determined the relative influence of the 45 elements, based on collected project data. The results showed a significant correlation between the estimate score and the accuracy of the estimate. Computer software, the Estimate Score Program (ESP), was developed to automate the scoring procedure, assess estimate accuracy, and predict contingency, based on historical cost data. The estimator can enter the base estimate into ESP and then rate the estimate, relative to each of the 45 elements. ESP automatically calculates the estimate score, as the user rates each element. The user can query the ESP historical database to view the estimate scores and estimate accuracy of similar projects. A cumulative probability S-curve, generated by ESP, is based on projects selected in the query and the estimate score of interest. The user can also predict the cost range—upper and lower limits—of a desired confidence level. ESP can be used to “check” the amount of contingency determined by other methods, as well as a method of predicting its own contingency.