Road tunnel construction is subject to underground uncertainties and risks, and as such it is difficult to predict the final construction cost, especially at the conception phase where issues are evaluated and important design decisions are made. A system assisting in the early cost estimation of road tunnels would therefore be of great value as it would allow the quick costing of alternative and more economical solutions. The development of such an early cost estimation system is discussed in this paper. First, the basic parameters (geological, geometrical, and work quantities-related) affecting temporary and permanent support and final construction cost are determined. After that, appropriate real-world data derived from the analysis of 33 twin tunnels of 46 km total length constructed for the Egnatia Motorway in northern Greece from 1998 to 2004 and related to work quantities is collected and normalized. Appropriate price lists are then applied to calculate the costs; subsequently, cost-estimat...

In projects with repeating activities (such as multistory buildings, highways, or pipelines consisting of reiterating identical or similar units) and in which the activity unit production rates are characterized by uncertainty or imprecision, fuzzy set theory and the well-established repetitive scheduling method (RSM) can be combined to ensure uninterrupted usage of resources between similar activities in different units. The reason for this approach is that in practice the application of RSM may be hindered by several considerations, for example, repetitive units may be slightly different from each other, the performance of construction crews may vary, and there may be complex resource matching and sharing between activities and work sites. The proposed methodology is termed fuzzy repetitive scheduling method (F-RSM), and it requires a generalization of RSM in which schedules are represented by two- or three-dimensional graphs and whereby the concepts of a control segment and the controlling sequence are...

Among prevailing contract‐awarding paradigms, the qualifications‐based selection (QBS) method offers awarding agencies a substantial level of flexibility and multifactored decision‐making capability over traditional cost‐based paradigms (such as competitive bidding). QBS is a process used by owners to select proposers to whom they can award a project contract, based on the proposers' professional qualifications in relation to the specific project parameters. Further to being very suitable for the selection of professional engineering design services, QBS can also be applied for the selection of contractors involved in specialized construction or design‐build projects. In such cases, the procurement method and the selection criteria used are critical decisions involving several key project team members and decision factors, the formulation of which requires complex evaluation methodologies. Within this context, a process is proposed by which the traditional QBS method can be improved via the utilization of a fuzzy Delphi method (FDM), a cross‐mutation of the traditional Delphi method (DM) and fuzzy logic (FL). The method utilizes ‘expert’ appraisals to identify both the factors and their weights in the overall evaluation process through a ‘fuzzy attractiveness ratio’ (FAR) which is used for evaluating the bidders' suitability to task.

To account for the skill of construction crew, an ad hoc coefficient is used as a multiplier of productivity estimations in most available methodologies today. However, little has been published on the determination of this coefficient and, moreover, on the way it is affected by repetition at the same working conditions. As such, research has been undertaken to associate the crew skill coefficient to the learning curve of a specific repetitive activity. Field measurements have been used as the basis to develop statistically sound datasets for subsequent use in simulation-based analysis. Field and simulation results have been compared through the use of an existing prototype simulation platform (CaissonSim) in order to determine the most appropriate crew skill coefficient. All developed models have been validated through the application of a strict statistical protocol. A sensitivity analysis has been undertaken in order to examine the cause of the productivity estimates’ variation. The main conclusions are that the crew skill coefficient depends on the learning rate and the determination of the standard production point, which denotes the termination of the learning phenomenon.