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.

Building renovation is an effective way to revive the use of a building, the use efficiency of which is primarily determined by its layout. However, in architectural practice, architects and building owners renovate buildings based on their personal subjective perceptions of how occupants use the building instead of systematically analyzing their use behaviors. This study proposes a model, called the Function-space Assignment and MOvement Simulation (FAMOS) model, which integrates radio frequency identification (RFID), fast messy genetic algorithms (fmGA), and movement simulation techniques to solve the function-space assignment problem. The RFID equipment is specifically used to track the occupants' movement data in a building, the fmGA is employed to identify the optimal result of function assignment, and the movement simulation technique is adopted to verify the result and support the decision-making of function-space assignment. This study presents a real case study to demonstrate the use of FAMOS and compare its assignments with those generated by a renovation architect. The objective function showed that FAMOS's version had a 14.80% higher objective value than the architect's version. The experiment also showed that FAMOS helped the architect find the best assignment or improve their assignment based on desired objectives such as preferred space size, minimized movement distance, or removal of corridor congestion.

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During the conceptual phase of a construction project, numerous uncertainties make accurate cost estimation challenging. This work develops a new model to calculate conceptual costs of building projects for effective cost control. The proposed model integrates four mathematical techniques (sub-models), namely, (1) the component ratios sub-model, (2) fuzzy adaptive learning control network (FALCON) and fast messy genetic algorithm (fmGA) based sub-model, (3) regression sub-model, and (4) multi-factor evaluation sub-model. While the FALCON- and fmGA-based sub-model trains the historical cost data, three other sub-models assess the inputs systematically to estimate the cost of a new pro­ject. This study also closely examines the behavior of the proposed model by evaluating two modified models without considering fmGA and undertaking sensitivity analysis. Evaluation results indicate that, with the ability to more thor­oughly respond to the project characteristics, the proposed model has a high probability of increasing estimation accura­cies more than the three conventional methods, i.e., average unit cost, component ratios, and linear regression methods.

Custom foot orthotics are commonly used in the treatment and prevention of a variety of medical conditions pertaining to the foot and overall body biomechanics. Traditionally, orthotics are made by vacuum forming material to a plaster cast of a foot. Podiatrists have control over the end result through the addition and subtraction of plaster from the cast. Digital scanning and computer aided surface modeling techniques are the current state-of-the-art in orthotics production, yet many podiatrists still prefer to use traditional plaster cast methods, partly due to the superior control afforded by plaster manipulation. We propose a novel state-of-the-art solution that achieves the necessary level of control over the orthotic's geometry in the form of input parameters.