The process of decision making and risk analysis are essential tasks along the construction project cycle. Over the years, construction practitioners and researchers have used various methods, tools and techniques to ...The process of decision making and risk analysis are essential tasks along the construction project cycle. Over the years, construction practitioners and researchers have used various methods, tools and techniques to evaluate risk and assist in making more concise decisions. Most practitioners, however, rely on their expert judgment, past experience, intuition, acquired and accumulated knowledge and gut feelings to make decisions. Aleatory （natural, heterogeneity and stochasticity） and epistemic （subjective, ignorance） are the two major types of uncertainties observed in natural sciences. Practitioners traditionally deal with aleatory uncertainty through probabilistic analysis based on historical data （frequentist approach）; and epistemic uncertainty, on the other hand, handled through the Bayesian approach which has limitations since it requires a priori assumption. This paper reports the application of the DST （Dempster Shafer Theory） of evidence to determine the most critical risk factors affecting project cost contingencies using their epistemic probabilities of occurrence. The paper further discuses how these factors can be managed to enhance successful delivery of infrastructural projects. It uses the mixed methodology, with data gathered through structured questionnaires distributed to construction clients, contractors, professionals and experts in the built environment. The research revealed that design risk, financial risk and economic risk were most important cost risk categorizations. In particular, scope changes, incomplete scope definition, incomplete design, changes in specification, micro and macroeconomic indicators and delayed payment problems were identified as the most important risk factors to be considered during the cost contingency estimation process, hence successful delivery of infrastructural projects. The paper concludes by recommending modalities for managing the contingency evolution process of risk estimation to enhance successful delivery and management of infrastructural projects.展开更多
The paper Proposes a colorfulness enhancement of pictorial images using image classifier based on chroma histogram. This approach firstly estimates strength of colorfulness of images and their types. With such determi...The paper Proposes a colorfulness enhancement of pictorial images using image classifier based on chroma histogram. This approach firstly estimates strength of colorfulness of images and their types. With such determined infomation, the algorithm automatical- ly adjusts image colorfulness for a better natural image look. With the help of an additional detection of skin colors and a pixel chroma adaptive local processing, the algodtlan produces more natural image look. The algorithm perfomance had been tested with an image quality judgment experiment of 20 persons. The experimental result indicates a better image preference.展开更多
文摘The process of decision making and risk analysis are essential tasks along the construction project cycle. Over the years, construction practitioners and researchers have used various methods, tools and techniques to evaluate risk and assist in making more concise decisions. Most practitioners, however, rely on their expert judgment, past experience, intuition, acquired and accumulated knowledge and gut feelings to make decisions. Aleatory （natural, heterogeneity and stochasticity） and epistemic （subjective, ignorance） are the two major types of uncertainties observed in natural sciences. Practitioners traditionally deal with aleatory uncertainty through probabilistic analysis based on historical data （frequentist approach）; and epistemic uncertainty, on the other hand, handled through the Bayesian approach which has limitations since it requires a priori assumption. This paper reports the application of the DST （Dempster Shafer Theory） of evidence to determine the most critical risk factors affecting project cost contingencies using their epistemic probabilities of occurrence. The paper further discuses how these factors can be managed to enhance successful delivery of infrastructural projects. It uses the mixed methodology, with data gathered through structured questionnaires distributed to construction clients, contractors, professionals and experts in the built environment. The research revealed that design risk, financial risk and economic risk were most important cost risk categorizations. In particular, scope changes, incomplete scope definition, incomplete design, changes in specification, micro and macroeconomic indicators and delayed payment problems were identified as the most important risk factors to be considered during the cost contingency estimation process, hence successful delivery of infrastructural projects. The paper concludes by recommending modalities for managing the contingency evolution process of risk estimation to enhance successful delivery and management of infrastructural projects.
基金supported by SystemIC 2010 Project(No.10030518),the MKE(Ministry of Knowledge Economy,Korea)
文摘The paper Proposes a colorfulness enhancement of pictorial images using image classifier based on chroma histogram. This approach firstly estimates strength of colorfulness of images and their types. With such determined infomation, the algorithm automatical- ly adjusts image colorfulness for a better natural image look. With the help of an additional detection of skin colors and a pixel chroma adaptive local processing, the algodtlan produces more natural image look. The algorithm perfomance had been tested with an image quality judgment experiment of 20 persons. The experimental result indicates a better image preference.
