Nang Fei Pan

Selecting an appropriate excavation construction method based on qualitative assessments

Choosing an appropriate excavation construction method is a key for successful completion of the project. However, such an evaluation involves a complex decision-making process associated with numerous uncertainty factors, imprecise information and judgments. The Analytical Hierarchy Process (AHP) has been widely applied to evaluate alternatives related to multiple decision criteria. Nevertheless, the AHP is incapable of dealing with the inherent subjectivity and ambiguity existing in the mapping of the decision-makers judgment to exact numerical values. This paper presents a fuzzy AHP approach to cope with this problem and be an attempt in the determination of a suitable excavation construction method. The approach employs triangular and trapezoidal fuzzy numbers and the @a-cut concept to better represent the degrees of uncertainty held by the decision-maker. A case study concerning a foundation construction project is presented to illustrate the salient features of the model. The results demonstrate the applicability of the method that can be used for effectively evaluating excavation construction alternatives.

Assessing Failure of Bridge Construction Using Fuzzy Fault Tree Analysis

Estimating exact probabilities of occurrence of bridge failure for the use in the conventional fault tree analysis (FTA) is difficult when fault events are imprecise such as human error. A fuzzy FTA model employing fuzzy sets and possibility theory to tackle this problem is proposed. An example of the collapse of cantilever gantry during construction demonstrates the capability of this approach that can assist safety engineer to better evaluate bridge performance.

Pavement performance prediction through fuzzy regression

Accurate predictions of future pavement conditions are essential for determining the most cost-effective maintenance strategy. The current methods for assessing pavement conditions involve either equipment measures or visual inspections. Equipment measures are not extensively implemented because of high cost; thus, subjective evaluations by road inspectors are often used as a replacement. Nevertheless, visual inspections could draw in errors and variations due to subjectivity and uncertainty. The present serviceability index (PSI), one of the most common indicators used to evaluate pavement performance, is incapable of transforming ones imprecise judgment into an exact number between 0 (the worst) and 5 (the best). Conventional regression cannot deal with visual inspection data that are linguistic or non-crisp. In contrast, fuzzy regression is capable of handling such fuzzy data. In this paper, pavement conditions are exemplified by five membership functions and estimated by using fuzzy regression to better account the uncertainties of the traditional method. Also, a similarity indicator is applied to measure the goodness of fit. A case study using pavement inspection data is presented to establish estimated fuzzy regression equations. The results demonstrate the capability of the model, which is able to assist road administration units to determine desirable repair actions regarding the predicted pavement conditions.

Evaluation of building performance using fuzzy FTA

Estimating exact probabilities of occurrence of fault events for use in the conventional fault tree analysis (FTA) is difficult when failure data are insufficient or fault events are imprecise such as human error. A hybrid approach employing fuzzy sets and possibility theory is proposed to overcome this problem. In this approach, failure possibilities of vague events are characterised by fuzzy sets to translate expert subjective judgements while fuzzy fault rates are derived from fuzzy possibility scores based on a transformed Onisawas possibility function. An example of beam failure illustrates the use of the approach and demonstrates the capability of the model that can assist safety engineers to effectively assess fault possibilities and better evaluate building performance.

Systematic image quality assessment for sewer inspection

Closed circuit television (CCTV) has been applied in many developing or developed counties for sewer inspection due to its low setup cost and technical requirement. Several automated diagnosis systems of sewer pipe defects had been developed to assist the technicians in interpreting or classifying sewer pipe defects. However, many researchers pointed out that good image quality is the prerequisite for accurate interpretation and diagnosis of CCTV inspection but has not a proper evaluation approach. In this paper, a CCTV image quality index considering both of the luminance distortion and the contrast distortion of a CCTV image compared by reference images is proposed and was applied to assess the image quality of the CCTV images shot for a sewer house-connection project. The experimental result indicates that rather than luminance contrast plays a more important role in the CCTV image quality that can be effectively improved by contrast enhancement. Since CCTV image quality can hardly distinguished by human eyes, the proposed image quality index can provide helpful information to efficiently assist the on-site technicians in precisely shooting better CCTV images for the pipe defection. Additionally, a sensitivity analysis of contrast stretch was implemented to quantify the CCTV image quality improvement. CCTV imaging conditions, such as pipe materials and imager status, are found as the factors affecting the CCTV image quality. In the future, a real-time CCTV image quality assessment will be developed by modifying the CCTV image quality index as an instantaneous reference for imaging adjustment that can be expected to be practicable for the on-site sewer inspection because of the extremely short computation time.

FEATURE EXTRACTION OF SEWER PIPE DEFECTS USING WAVELET TRANSFORM AND CO-OCCURRENCE MATRIX

In general, the sewer inspection usually employs a great number of CCTV images to discover sewer failures by human interpretation. A computer-aided program remains to be developed due to humans fatigue and subjectivity. To enhance the efficiency of sewer inspection, this paper attends to apply artificial intelligence to extract the failure features of the sewer systems that is demonstrated on the sewer system in the eastern Taichung City, Taiwan. Wavelet transform and gray-level co-occurrence matrix, which have been widely applied in many texture analyses, are adopted in this research to generate extracted features, which are the most valuable information in pattern recognition of failures on CCTV images. Wavelet transform is capable of dividing an image into four sub-images including approximation sub-image, horizontal detail sub-image, vertical detail sub-image, and diagonal detail sub-image. The co-occurrence matrices of horizontal orientation, vertical orientation, and 45° and 135° orientations, respectively, were calculated for the horizontal, vertical, and diagonal detail sub-images. Subsequently, the features including angular second moment, entropy, contrast, homogeneity, dissimilarity, correlation, and cluster tendency, can be obtained from the co-occurrence matrices. However, redundant features either decrease the accuracy of texture description or increase the difficulty of pattern recognition. Thus, the correlations of the features are estimated to search the appropriate feature sets according to the correlation coefficients between the features. In addition, a discriminant analysis was used to evaluate the discriminability of the features for the pipe failure defection, and entropy, correlation, and cluster tendency were found to be the best features based on the discriminant accuracy through an error matrix analysis.

Forecasting bridge deck conditions using fuzzy regression analysis

Abstract A good bridge management system requires an accurate and efficient bridge performance and prediction model. Ordinary regression models have been extensively used to forecast future infrastructure conditions. However, data on current bridge conditions obtained from inspectors are inherently subjective and non‐crisp; thus ordinary regression techniques are incapable of predicting future bridge performance when inspection data are not numerical. This paper presents a multiple fuzzy linear regression model for the estimation of bridge deterioration conditions. The proposed model can effectively tackle non‐crisp data and a mixture of fuzzy data and crisp data. An empirical case study using bridge inspection data from Taiwan is used to examine the variables contributing to deterioration of concrete decks. The results demonstrate the capability and effectiveness of the proposed model, which can assist bridge managers to better predict bridge deck performance.

Web-based radio frequency identification facility management systems

The operational life of a facility is extended through regular functional maintenance. The objective of this study is to enhance facility management efficiency using radio frequency identification (RFID) technology. A data management module is first developed to collect maintenance records. A statistical module is then established to graphically display the collected data. Maintenance activities are sequenced using a scheduling module to ensure that the facility functions normally. A forecast module is developed using fuzzy neural networks to avoid facility malfunction before the next maintenance. These four modules are integrated into a web-based RFID facility management system. System performance is validated using an actual building. Two RFID apparatuses are invented based on operational needs when implementing the developed system. The validation results show that integrating RFID technology with a web-based system, database, scheduling theory, artificial intelligence and enhancing apparatuses can improve facility maintenance efficiency. This study is one of the first researches providing a comprehensive solution for enhancing facility management efficiency.

Decision Making Behaviors in Planning Green Buildings

A green building is an outcome of a design philosophy which focuses on increasing the efficiency of resource use. This paper presents a conceptual model in simulating the risk behaviors of decision makers in influencing the decision making of selecting green building designs by using 3 difference processes including benefit-cost analysis, multi-criteria decision-making, and Nash equilibrium game. The proposed approach allows the project owner and the consultant to assess the green building cost and effectiveness of performance for different design alternatives during the early design stage. A two-person nonzero sum game is considered in this paper to model the interactions between both players with respect their different utilities and different risk behaviors. The results of the case study show the consultant plays an important role in influencing the developer¡¯s choice of selecting a desirable green building design alternative.

Learning construction procurement negotiation in an educational game

This study aimed to establish a conceptual model for negotiation games, in which multiple opportunities for engineering projects were provided, and players were thus allowed to improve the negotiation profits by integrating different procurement items from different projects. Students were able to play the role of a constructor or a supplier, distinguished by the initial capital or score. Accordingly, the constructor and the supplier reached different outcomes in the game, and the outcomes were put under evaluation and compared with the achievements of all players. The game adopted a rule that both sides of the players kept track of the negotiation process, either with props or on a written recording form. The game was administered in a classroom setting, followed by the administration of a questionnaire, an interview and a test for further exploration on the learning efficacy of this game model.