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To properly plan for construction, repair, maintenance, and reconstruction of highways the minimum acceptable roadway condition is needed information. This, along with other pavement management tools, will help select the most desirable roadway alternatives. In this research the minimum acceptable conditions are developed based on an opinion survey of non-technical but high-level decision makers. Roadway roughness, expressed as international roughness index (IRI), is used as the measurement criteria. Because IRI is a widely known, acceptable, and a uniformly measurable index, it is used for the purpose of this research. The minimum IRI values developed here will help managers, planners, and engineers in prioritizing their plans and projects. Iran has a central planning system, hence having a minimum acceptable IRI will help in producing homogeneity in decision making. A questionnaire is sent to top level and influential managementlevel officials who have a decisive input in highway matters. The officials are asked to choose the minimum acceptable service level of different types of roadways and classifications. Naturally, roadways with higher levels of importance would require higher service levels. The answers to the survey questionnaires are investigated to determine a preferred minimum acceptable roadway condition. The IRI is computed using a mechanical device enabling a more uniform data collection. The IRI was first proposed by The World Bank as a standard roughness statistic. Extensive research has proven that the IRI can be related to pavement condition. The result of the opinion survey is investigated to determine the minimum levels acceptable for each category. The responses show distinct preference patterns for most of the roadway types. Survey results are investigated by plotting and analyzing them. Based on road user’s perception of roadway condition using guidelines from AASHTO, the Corp of Engineers, and related research work. The appropriate IRI limits and ranges are determined for Iran’s highways. These values are adjusted to obtain final values for Iran. The result, shown in a table, gives upper and lower IRI values accepted and recommended for Iran’s highways. The result of this research work is specifically useful in developing specifications for new pavement design, accepting new pavement from contractors, pavement management, highway planning, and in roadway life cycle cost analysis decision making. The results are subject to refinement over time.

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The use of Value Engineering (VE) methodology in construction industry has grown significantly, mainly in view of its extensive benefits. The main task in evaluation phase of VE workshop is to assess alternative ideas, proposed for each function. This phase of VE, hence, could be deemed as a Multi Criteria Decision Making (MCDM) problem. This paper presents a fuzzy decision support system (DSS) to be employed in evaluation phase of VE. The proposed multi alternative decision model may be recommended where alternatives’ preferences ratios are different, and scores assigned to each alternative idea are uncertain. As use of VE has greater payoffs at the earlier stages of the construction projects, in which most of the criteria are still vague and not precisely defined, exploiting this DSS may result in more tangible model of decision making process and satisfactory outlook of VE studies in construction projects. A ranking methodology in a spreadsheet template is also provided to facilitate the ranking process. Performance of the proposed methodology is tested using a case example in the tunneling industry.

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Presence of risks and uncertainties inherent in project development and implementation plays

significant role in poor project performance. Thus, there is a considerable need to have an effective risk

analysis approach in order to assess the impact of different risks on the project objectives. A powerful risk

analysis approach may consider dynamic nature of risks throughout the life cycle of the project, as well as

accounting for feedback loops affecting the overall risk impacts. This paper presents a new approach to

construction risk analysis in which these major influences are considered and quantified explicitly. The

proposed methodology is a system dynamics based approach in which different risks may efficiently be

modeled, simulated and quantified in terms of time, cost and quality by the use of the implemented object

oriented simulation methodology. To evaluate the performance of the proposed methodology it has been

employed in a bridge construction project. Due to the space limitations, the modeling and quantification

process for one of the identified risks namely “pressure to crash project duration” is explained in detail.

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In the early stages of a construction project, the reliability and accuracy of conceptual cost estimates are major concerns for clients and cost engineers. Previous studies applied scoring methods and established common rules or mathematical methods to assess the quality of cost estimates. However, those approaches have some limitations in adapting to real-world projects or require understanding of sophisticated statistical techniques. We propose a Conceptual Cost Estimate Reliability Index (CCERI), a simple, easy-to-use, and easy-to-understand tool that incorporates weights for 20 factors influencing the quality of conceptual cost estimates. The weights were obtained by eliciting experts’ experience and knowledge. Cost data from 71 building projects were used in the analysis and validation of the CCERI. The analysis reveals that a conceptual cost estimate with a CCERI score of less than 3000 has a high probability of exceeding 10% error, and such conceptual cost estimates are unlikely to be reliable. With the CCERI score, a decision maker or a client can recognize the reliability of the conceptual cost estimates and the score can thus support decision making using conceptual cost estimates. In addition, with the CCERI and the relative importance weights of factors affecting the conceptual cost estimates, the estimator can find ways to modify a conceptual cost estimate and reestimate it. These alternatives can decrease the risk in the conceptual estimated cost and assist in the successful management of a construction project.

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The construction temporary facilities layout planning (CTFLP) requires an identification of necessary construction temporary facilities (CTFs), an identification of candidate locations and a layout of CTFs at candidate locations. The CTFLP is particularly difficult and complex in large-scale construction projects as it affects the overall operation safety and effectiveness. This study proposes a decision making system to decide on an appropriate CTFLP in large-scale construction projects (e.g. dams and power plants) in a comprehensive way. The system is composed of the input, CTF identification, candidate location identification, layout optimization, evaluation and selection, as well as output stages. The fuzzy logic is employed to address the uncertain factors in real-world situations. In the input stage, the knowledge bases for identifying CTFs and candidate locations are determined. Then, CTFs and candidate locations are identified in the following two stages. In the mathematical optimization stage, a multiobjective mathematical optimization model with fuzzy parameters is established and fuzzy simulation-based Genetic Algorithm is proposed to obtain alternative CTFLPs. The intuitionistic fuzzy TOPSIS method is used to evaluate and select the most satisfactory CTFLP, which is output in the last stage. To demonstrate the effectiveness and efficacy of the proposed method, the CTFLP for the construction of a large-scale hydropower dam project is used as a practical application. The results show that the proposed system can assist the contractor to obtain an appropriate CTFLP in a more efficient and effective manner.

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The fresh properties of Self Compacting Concrete (SCC) might be more susceptible to quality and quantity changes of ingredients than conventional concrete because of a combination of detailed requirements, more complex mix design, and inherent low yield stress and viscosity. In spit of the low robustness of SCC, there are a few methods available to assess the SCC robustness that the accuracy of these methods has not been fully agreed. The current study provides an index for SCC robustness based on the rheology parameters. Thus, an experimental program was undertaken to evaluate the robustness of eight selected SCCs. For doing this, water content of each SCC was changed slightly and their fresh and hardened properties were measured. The results indicated that the length of rheology parameters curve due to variation of mixing water is able to assess the SCC robustness that is comparable with combined performance based on the workability tests changes. According to this index, the robustness of SCC increases about 10% by using air-entraining admixture (AEA) and decreases considerably by reduction the paste volume (up to about 5 times). Also, the most appropriate single workability test to assess the robustness is sieve segregation test. Moreover, the scattering of compressive strength results show that there is a level of robustness in fresh state that after that the scattering of results in hardened state can be affected.