Labor Performance Analysis for Microtunneling Projects

Microtunneling projects have special process characteristics. The production time of microtunneling projects is needed for project estimating and scheduling. The production time has three basic components: (1) preparation times for pipe segments; (2) preparation times; and (3) delay times. Real project data were collected from 35 projects to predict the time needed for pipe segment preparation. A probabilistic distribution technique was used to predict the preparation times for pipe segments. Various numbers of probabilistic distributions were tested against the data. A log-logistic distribution was selected to represent the preparation time’s probability for a pipe in microtunneling projects. Preparation times for high, medium, and low performance contractors were predicted. By enhancing the estimation process of the microtunneling projects, contractors can use this paper in predicting the preparation time for pipe segments, which represents the crew (or crew’s) productivity.     

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.     

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.     

Causes of Delay in Building Construction Projects in Egypt

Delay in construction projects is considered one of the most common problems causing a multitude of negative effects on the project and its participating parties. This paper aims to identify the main causes of delay in construction projects in Egypt from the point of view of contractors, consultants, and owners. A literature review was conducted to compile a list of delay causes that was purged based on appropriateness to Egypt in seven semistructured interviews. The resulting list of delay causes was subjected to a questionnaire survey for quantitative confirmation and identification of the most important causes of delay. The overall results indicated that the most important causes are: financing by contractor during construction, delays in contractor’s payment by owner, design changes by owner or his agent during construction, partial payments during construction, and nonutilization of professional construction/contractual management. The contractor and owner were found to have opposing views, mostly blaming one another for delays, while the consultant was seen as having a more intermediate view. Results’ analyses suggest that in order to significantly reduce delay a joint effort based on teamwork is required. Furthermore, causes of project delay were discussed based on the type and size of the project.     

Avoiding and Mitigating Delay and Disruption Claims Conflict: Role of Precontract Negotiation

Delay and disruption claims often generate conflict and contract dispute in the delivery of building and civil engineering projects. If construction delay claims conflict can be avoided or mitigated, there could be substantial financial savings on projects. This study explores the effect of precontract negotiation as a means of avoiding or mitigating delay and disruption claims conflict. The data collection instrument was a structured questionnaire administered face to face on 41 contractors’ personnel on 41 completed projects in Singapore. The data were analyzed using structural modeling with partial least squares estimation approach. The results indicate that when the contractors received an unfavorable outcome from the contract administrator’s decision on their claims for delay, the intensity of conflict was lower when there was precontract negotiation and precontract agreement regarding the rules for quantifying and assessing the impact of anticipated delays than when there was none. It was also discovered that the higher the level of precontract negotiation and precontract agreement on the rules for quantifying and assessing delays, the higher the contractors perceived the quality of the decision-making process for delay claims during the construction phase. Further, the higher the contractors perceived the quality of the decision-making process for delay claims, the lower the intensity of conflict. At the time of entering into contracts, owners and their project management team need to pay more attention to precontract negotiation and agreement with their contractor to clarify and agree on the rules for quantifying and assessing the impact of anticipated delay and disruption. Aspects that require precontract negotiation, agreement, and clarification include: the rules of evidence for claims, the record requirements for claims and the procedure for keeping the records, form of construction program including the software for the preparation of the program and the procedure updating the program, the methodology for analyzing delay claims, formula for quantifying unabsorbed head office overhead component of prolongation cost, the method for quantifying disruption cost, the handling of concurrent delays, profit—whether claimable and the rate of profit to be paid, acceleration—circumstances under which it will be compensated and basis of compensation, and the question of who owns the float. These are, typically, not adequately covered by most standard forms of contracts. The agreements on these matters may be incorporated as part of partnering agreement or as a supplement to the contract agreement. Precontract negotiation, clarity, and agreements could produce instrumental and noninstrumental (social psychological) effects, which could facilitate delay and disruption claims assessment and their resolution. It could mitigate conflict even when the outcomes are unfavorable to a party.     

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.     

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.     

Economic Value of Combined Best Practice Use

This paper measures the value of best practices based on data taken from the Construction Industry Institute Benchmarking and Metrics database. A three-step process provides the basis for measuring the potential benefits of increased best practice use. First, a practice use index is derived to model the way in which best practices are utilized on actual projects. The index combines data from eight best practices to create a single measure of practice utilization. Second, a project performance index, combining cost and schedule metrics, is derived as an indicator of overall project performance. Third, the practice use index is correlated with cost, schedule, and overall project performance metrics. Both owners and contractors benefit from increased practice use. Potential cost savings for owners range from $1.7 to $3.4 million, depending on industry group and project size. Potential cost savings for contractors are higher, averaging $7.2 million for the typical $88 million heavy industrial project. Benefits from schedule reductions are most apparent for owners. On large building projects, schedule reductions for owners average 27 weeks. Finally, improvements in overall project performance on the order of 30% are observed for both owners and contractors.     

Applying a Genetic Algorithm-Based Multiobjective Approach for Time-Cost Optimization

Reducing both project cost and time (duration) is critical in a competitive environment. However, a trade-off between project time and cost is required. This in turn requires contracting organizations to carefully evaluate various approaches to attaining an optimal time-cost equilibrium. Although several analytical models have been developed for time-cost optimization (TCO), they mainly focus on projects where the contract duration is fixed. The optimization objective in those cases is therefore restricted to identifying the minimum total cost only. With the increasing popularity of alternative project delivery systems, clients and contractors are targeting the increased benefits and opportunities of seeking an earlier project completion. The multiobjective model for TCO proposed in this paper is powered by techniques using genetic algorithms (GAs). The proposed model integrates the adaptive weights derived from previous generations, and induces a search pressure toward an ideal point. The concept of the GA-based multiobjective TCO model is illustrated through a simple manual simulation, and the results indicate that the model could assist decision-makers in concurrently arriving at an optimal project duration and total cost.     

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.     

Organizing Constructability Knowledge for Design

Construction contractors have significant constructability expertise to contribute to the design process of projects. To utilize this expertise most effectively, the right information must be made available to the design team at the proper point in time and at the appropriate level of detail. Current methods for utilizing construction knowledge in design have made significant advances to improving projects. However, they are typically rudimentary: unstructured, not very efficient, and rely heavily on reviews. Organizing constructability information according to its use in the design process will allow project teams to take the best advantage of the construction expertise. This paper introduces a model for organizing constructability information based on timing and levels of detail. The model differs from current approaches because of this focus. How the model was developed is described. It is tested on six case study projects to assess applicability on different projects. An illustrative example is provided using a detailed case study of the Pentagon renovation project to show how the model can be used as a metric to guide constructability input during design.     

A Fuzzy Decision Framework for Contractor Selection

Contractor selection is the process of selecting the most appropriate contractor to deliver the project as specified so that the achievement of the best value for money is ensured. Construction clients are becoming more aware of the fact that selection of a contractor based on tender price alone is quite risky and may lead to the failure of the project in terms of time delay and poor quality standards. Evaluation of contractors based on multiple criteria is, therefore, becoming more popular. Contractor selection in a multicriteria environment is, in essence, largely dependent on the uncertainty inherent in the nature of construction projects and subjective judgment of decision makers (DMs). This paper presents a systematic procedure based on fuzzy set theory to evaluate the capability of a contractor to deliver the project as per the owner’s requirements. The notion of Shapley value is used to determine the global value or relative importance of each criterion in accomplishing the overall objective of the decision-making process. The research reported upon forms part of a larger study that aims to develop a fuzzy decision model for construction contractor selection involving investigating multiple criteria selection tendencies of construction clients, relationship among decision criteria, and construction clients’ preferences of criteria in the contractor selection process. An illustration with a bid evaluation exercise is presented to demonstrate the data requirements and the application of the method in selecting the most appropriate contractor for the project under uncertainty. The proposed model is not intended to supplant the work of decision-making teams in the contractor selection process, but rather to help them make quality evaluations of the available candidate contractors. One major advantage of the proposed method is that it makes the selection process more systematic and realistic as the use of fuzzy set theory allows the DMs to express their assessment of contractors’ performance on decision criteria in linguistic terms rather than as crisp values.     

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.     

Model for Predicting the Performance of Project Managers at the Construction Phase of Mass House Building Projects

The need to match project managers’ (PMs) performance measures onto projects of both unique and similar characteristics has long since been acknowledged by researchers. The need for these measures to reflect the various phases of the project life cycle has also been contended in the recent past. Here, a competency-based multidimensional conceptual model is proposed for mass house building projects (MHBPs). The model reflects both performance behaviors and outcome in predicting the PMs’ performances at the conceptual, planning, design, tender, construction, and operational phases of the project life cycle. Adopting a positivist approach, data elicited for the construction phase is analyzed using multiple regression techniques (stepwise selection). Out of a broad range of behavioral metrics identified as the independent variables, the findings suggest the best predictors of PMs’ performances in MHBPs at the construction phase are: job knowledge in site layout techniques for repetitive construction works; dedication in helping works contractors achieve works schedule; job knowledge of appropriate technology transfer for repetitive construction works; effective time management practices on house units; ability to provide effective solution to conflicts, simultaneously maintaining good relationships; ease with which works contractors are able to approach the PM and volunteering to help works contractors solve personal problems. ANOVA, multicollineriality, Durbin–Watson, and residual analysis, confirm the goodness of fit. Validation of the model also reflected reasonably high predictive accuracy suggesting the findings could be generalized. These results indicate that the model can be a reliable tool for predicting the performance of PMs in MHBPs.     

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.     

Risk Assessment Model of Tendering for Chinese Building Projects

This paper presents a risk assessment model for tendering of Chinese building projects on the basis of identification and evaluation of the major risk events in the Chinese construction market, investigations and interviews from which the factors inducing the risk events were determined, questionnaires on building projects within China’s borders, and the logistic regression method. The findings show that, to a certain extent, the risk of tendering for projects and the risk of a contracted project can be assessed through analysis of factors such as owner type, source of project financing, existence or lack of past cooperation between contractors and owners, the intensity of competition for tendering, the reasonableness of the bid price, and the degree of support from the contracting company to its projects. The model can serve as a supplementary tool for Chinese contractors in making decisions for project tendering within Chinese borders. At the same time, it is of reference significance for international contractors, enabling them to further understand the risks in the contract market for Chinese building projects.     

Hybrid Prequalification-Based, Innovative Contracting Model Using AHP

Design-build, cost-plus-time, and warranty are three innovative contracting methods used by the Federal Highway Administration (FHwA). Because of the challenges inherent in the nature of these approaches, contractors need to be capable and experienced to successfully deliver projects. It is then necessary to prequalify contractors to reduce the inherent risk of a project. Therefore, the objective of the research project described in this paper is to provide a methodology that will help public owners prequalify and select contractors within the context of the three delivery methods mentioned above. As a result, a hybrid prequalification and selection model to screen contractors who want to submit bids on public projects using any of the three innovative contracting methods above was developed. The analytical hierarchy process (AHP) was used to make prequalification decisions when multiple criteria are used. In addition, three case studies were developed to evaluate the application of AHP to the prequalification criteria in the three innovative contracting methods. AHP was utilized as an effective prequalification decision-making tool that eliminates subjectivity and produces decisions built on consistent judgments.     

Estimating Project S-Curves Using Polynomial Function and Neural Networks

The S-curve is a graphical representation of a construction project’s cumulative progress from start to finish. While S-curves for project control during construction should be estimated analytically based on a schedule of activity times, empirical estimation methods using various mathematical S-curve formulas have been developed for initial planning at predesign stages, with the mean for past similar projects often used as the basis of prediction. In an attempt to make an improvement, a succinct cubic polynomial function for generalizing S-curves is proposed and a comparison with existing formulas shows its advantages of accuracy and simplicity. Based on an analysis of the attributes and actual progress of 101 projects, four factors, i.e., contract amount, duration, type of work, and location, are then used as the inputs of a model developed for estimating S-curves as represented by the polynomial parameters. For model development, it is proposed to use neural networks for their ability to perform complex nonlinear mapping. The neural network model is compared with statistical models with respect to modeling and testing accuracy. The results show that the presented methodology can achieve error reduction consistently, thereby being potentially useful for owners and contractors in early financial planning and checking schedule-based estimates.     

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.     

SIM-UTILITY: Model for Project Ceiling Price Determination

Before considering bids submitted by competing contractors for a public procurement project, the owner should determine a project ceiling price or cost estimate to use as a reference point for evaluating the bids. A high ceiling price conflicts with the owner’s interests in minimizing costs. Meanwhile, a low ceiling price can jeopardize the project if all bids exceed the ceiling price. This paper proposes a model for determining a reasonable project ceiling price. The model, called SIM-UTILITY, is based on a utility theory and facilitated by a cost simulation approach. The utility theory is applied to reflect the owner’s preferences regarding the determination criteria, while the simulation approach is used to generate more objective project cost data to support execution of the utility theory. The advantages of SIM-UTILITY are proven by its successful application to three construction projects in Taiwan. A computerized SIM-UTILITY is expected to be broadly applicable to public construction projects in Taiwan.