Browsing by Author "Hsiao, Fan-Yi"

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An Activity-Based Simulation Model for Assessing Function Space Assignment for Buildings: A Service Performance Perspective
(2015-12-01)
Dzeng, Ren-Jye
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Wang, Wei-Chih
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Hsiao, Fan-Yi
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Xie, Yan-Qing
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土木工程學系
Function space assignment determines the function of all spaces and is essential to the design of a remodeled building. Optimization of function space assignment that relies upon users\' activities is difficult due to the lack of quantifiable and detailed data about users\' activities. In this paper, an activity-based simulation process model consisting of five phases is proposed and applied to an actual project. The phases include activity data collection, activity simulation, space movement pattern mining, function-space assignment optimization, movement simulation and assessment. The process also allows the building administrator to adjust the optimized assignment to accommodate hard-to-quantify preferences via a variety of indexes such as movement distance, corridor flow, and other space usage-related indexes. Results from application of the proposed model to an actual remodeling project indicated that the model offered the administrator a better assignment than the contracted architect, with a 5.79% increase in the objective function and a 23.92% decrease in movement distance. The adjusted assignment based on service-related indices, such as noise interference, further improved service by accounting for parameters that were not easily included in the objective function for optimization.
Application of RFID tracking to the optimization of function-space assignment in buildings
(2014-04-15)
Dzeng, Ren-Jye
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Lin, Chong-Wey
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Hsiao, Fan-Yi
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土木工程學系
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Department of Civil Engineering
Function-space assignment, which allocates a function for each space in a facility, is one of the most important factors in determining the usability performance of a building. Most architects renovate a building based on their personal perception of how the occupants might use the building instead of quantitatively analyzing their use behaviors. This study developed a function-space assignment optimization model based on the occupants' movement data as tracked by REID technology. The model mines the movement data by constructing patterns and calculating the relation values between functions. The search for the best assignment is based on the fast messy genetic algorithm (fmGA) with the objective function incorporating the preference of space size and the minimization of the distance for movement required by the occupants during the performance of their daily activities. The proposed model incorporated building-block filtering mechanism in the fmGA problem-solving process to generate enough copies of the good building blocks so more copies would remain for subsequent processing. The paper also describes two experiments that evaluate the performance of the model and compare the performances of the models with and without the building-block filtering mechanism. (C) 2014 Elsevier B.V. All rights reserved.
CONCEPTUAL COST ESTIMATIONS USING NEURO-FUZZY AND MULTI-FACTOR EVALUATION METHODS FOR BUILDING PROJECTS
(2017)
Wang, Wei-Chih
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Bilozerov, Tymur
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Dzeng, Ren-Jye
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Hsiao, Fan-Yi
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Wang, Kun-Chi
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, fuzzy adaptive learning control network (FALCON) and fast messy genetic algorithm (fmGA) based sub-model, (2) 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.
Function-space assignment and movement simulation model for building renovation
(2015-07-04)
Dzeng, Ren-Jye
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Wang, Wei-Chih
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Hsiao, Fan-Yi
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土木工程學系
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Department of Civil Engineering
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.
Neuro-Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction
(2012-11-01)
Hsiao, Fan-Yi
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Wang, Shih-Hsu
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Wang, Wei-Chih
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Wen, Chao-Pao
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土木工程學系
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.