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- 저자 : Chou Jui-Sheng (검색결과 4 건)
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Jui-Sheng Chou,김창완,Pei-Yu Tsai,Chun-Pin Yeh,손효주 대한토목학회 2017 KSCE JOURNAL OF CIVIL ENGINEERING Vol.21 No.6
This article compares Taiwan and Korea high speed rail systems to identify service factors that affect their long-term development. Data were collected by questionnaires and interviews administered over several years to study passenger travel behavior and perceptions of service quality. Analysis results indicate that improving operational effectiveness requires enhanced service quality and that Taiwan High Speed Rail (THSR) passengers are very concerned about facility of infrastructure services. In contrast, the main concern of Korea Train eXpress (KTX) passengers is frontline staff interaction. Notably, the data show that THSR passenger satisfaction increased steadily to levels superior to those expressed by KTX passengers. Another finding is that, in terms of resource allocation, both Taiwan and Korea should improve the handling of passenger complaints, provide improved scheduling information, and strive to improve arrival and departure punctuality. The contribution of this study is the development of a systematic method of assessing the long-term performance of high-speed rail transport services, by which management units can adjust operating strategies to continuously improve services. The analysis results can facilitate the THSR and KTX to formulating planning and operational strategies that can achieve the goal of sustainable operations.
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Probabilistic Multiobjective Optimization of Sustainable Engineering Design
Jui-Sheng Chou,Thanh-Son Le 대한토목학회 2014 KSCE JOURNAL OF CIVIL ENGINEERING Vol.18 No.4
As the global population reaches 7 billion and standards of living are increasing, engineers are being pressured to use limitednatural resources to satisfy ever-increasing demand. Project engineers are currently charged with achieving a balance between costand duration, and must consider environmental factors to reach sustainable development. This work proposes a novel probabilisticmulti-objective optimization algorithm to attain sustainable construction cost, project duration, and CO2 emissions simultaneously inan uncertain project environment. The novel algorithm, which is based on Particle Swarm Optimization integrated with Monte Carlosimulation, is applied to generate a low-carbon economy and cleaner production. A typical construction project is selected todemonstrate the application of proposed algorithm for making sustainable decisions under multi-objectives. The proposed methoddemonstrated its risk analysis capacity by obtaining non-dominant optimized solutions for cleaner construction. This papercontributes to facilitating project managers in achieving a design that satisfies technical and quality requirements with lowest cost,shortest duration, and minimal adverse impacts on the environment.
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Chou Jui-Sheng,Chen Li-Ying,Liu Chi-Yun 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.1
Regulations mandate testing concrete’s compressive strength after the concrete has cured for 28 days. In the ideal situation, cured strength equals the target strength. Advanced estimation of concrete’s compressive strength can facilitate quality management, improve safety, and present economic advantages in sustainable use. Basic statistical methods cannot effectively predict concrete’s strength or its non-linear relationships with the proportions of its constituent materials. In this study, a baseline model for predicting concrete’s compressive strength was constructed using a state-of-the-art machine-learning method. Most related studies have used sets of concrete mix design results concerning concrete specimens for laboratory-produced concrete specimens as training sets and have obtained simple models through regression; however, these models have been unsuitable for onsite prediction of the compressive strength of concrete with the various mix designs. Control over mix proportions is high in laboratories, resulting in low variation; onsite manual operation and environmental factors cause significant variations in assessment data. In this study, machine-learning techniques and a newly developed metaheuristic optimization algorithm were applied to big long-term data from 75 concrete plants to construct the optimal machine-learning model. Our self-developed forensic-based investigation algorithm was employed to fine-tune the hyperparameters of the extreme gradient boosting model and to improve the model’s generalizability. The lowest mean absolute percentage error (MAPE) obtained using this model was 9.29%, which was smaller than the lowest MAPE achieved using the conventional simple regression with the water-to-binder (W/B) ratio (12.73%). The traditional method tends to overestimate the actual compressive strength. Finally, a convenient expert system was developed that facilitates the use of the proposed model by onsite engineers for quality management. This system expedites the judgment of whether a mixed design is reasonable, reducing production costs while maintaining the safety of concrete structures. It can be widely applied in practice and function as an effective decision-making tool.
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Collin Koranda,Wai Kiong Chong,김창완,Jui-Sheng Chou,김창민 대한토목학회 2012 KSCE JOURNAL OF CIVIL ENGINEERING Vol.16 No.5
Sustainability and lean concepts can both be applied to the construction industry to help minimize waste. Although both concepts work to alleviate similar problems, organizations struggle to integrate the concepts. This paper examined projects of different sizes and in different environments within the Midwestern United States to determine what aspects hinder the integration of sustainability and lean concepts within the region. Professionals associated with the industry were interviewed to identify sources of waste for lean and sustainable projects. From the case studies, various aspects of waste that exist in construction projects were recognized, and a comparison of the interaction of lean and sustainable concepts was documented. A process for planning throughout the entire construction process was determined so that waste can be reduced and the integration of lean and sustainable concepts is more achievable.
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