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Semiconductor hookup construction (i.e., constructing process tool piping systems) is critical to semiconductor fabrication plant completion. During the conceptual project phase, it is difficult to conduct an accurate cost estimate due to the great amount of uncertain cost items. This study proposes a new model for estimating semiconductor hookup construction project costs. The developed model, called FALCON‐COST, integrates the component ratios method, fuzzy adaptive learning control network (FALCON), fast messy genetic algorithm (fmGA), and three‐point cost estimation method to systematically deal with a cost‐estimating environment involving limited and uncertain data. In addition, the proposed model improves the current FALCON by devising a new algorithm to conduct building block selection and random gene deletion so that fmGA operations can be implemented in FALCON. The results of 54 case studies demonstrate that the proposed model has estimation accuracy of 83.82%, meaning it is approximately 22.74%, 23.08%, and 21.95% more accurate than the conventional average cost method, component ratios method, and modified FALCON‐COST method, respectively. Providing project managers with reliable cost estimates is essential for effectively controlling project costs.

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Design delays can negatively influence the total completion time of a facility construction project. Knowing the factors to which design delays are most sensitive supports the time management of designs. However, factors that cause design delays are several and interrelated. This study proposes a new model to identify key factors that drive design delays. The core of the model integrates importance-satisfaction analysis (ISA) and an influence-relations map (IRM). The ISA evaluates the performance of each delay factor, while the IRM captures the causal relationships among factors. Additionally, the IRM is generated using a decision making trial and evaluation laboratory technique (DEMATEL). The model is applied to a real-world high-tech facility construction project to indicate the strengths of the model. In this investigation, four first-level delay factors and 17 second-level delay sub-factors are derived. The factor of “organization's decision making and budget constraints” is identified as the key driver of design delays in the project of interest. The results support management in determining which problem factors should be given priority attention. The proposed model can be employed in other decision-making situations that involve interrelated factors.

-In recent years, the construction industry has been actively seeking for possible applications of Building Information Modeling (BIM), including the domain of building disaster-prevention management. BIM enables us to present a facility layout in threedimension (3D) and carry the disaster-prevention objects and information whereas the traditional approach can only be presented in two-dimension. Therefore, it could be possible to improve the traditional two-dimensional (2D) plane of building disaster-prevention management. By focusing on fire disaster, this study develops a BIM-based model to support disaster-prevention management. This model includes four modules: personnel safety evaluation, escape route planning, education and training, and equipment maintenance modules. The personnel safety evaluation module integrates BIM with a type of fire simulation software (Fire Dynamics Simulator), while the escape route planning module and safety education and training module help generate escape routes and emergency exits according to the 3D functions of BIM. The firefighting equipment maintenance module utilizes the information management ability provided by the BIM to demonstrate 3D location information and condition (maintenance records) of all fire equipment, to increase the quality of the safety education, and ensuring the all fire equipment are maintained in great condition. Above all, this proposed model is applied to a high-tech facility for testing its feasibility.