Jyh-Bin Yang

Applying case-based reasoning technique to retaining wall selection

Abstract Case-based reasoning (CBR) approach recalls and learns from previous cases to resolve or provide recommendations for current problems. This study presents a case-based retaining wall selection system (CASTLES) in which the case base consists of 254 previous retaining wall cases in design reports in Taiwan. According to the ability of the users input to accurately describe the characteristics of a new project and a predefined similarity function, CASTLES identifies a set of feasible retaining-wall systems from the case base. Comparing CASTLES with four actual field cases reveals that the case-based reasoning approach is highly promising for selecting retaining wall systems.

Benefit Analysis of Knowledge Management System for Engineering Consulting Firms

AbstractEngineering consulting (EC) firms in the construction industry are knowledge-intensive and experience-based organizations. As the knowledge accumulated from previous experiences is the essential asset for wining and managing future projects, many EC firms have developed diversified knowledge management systems (KMSs) to accumulate and retain this essential knowledge. However, evaluation methods and results regarding the benefit analysis of KMSs are rarely found in literature. To evaluate the performances of KMSs, most previous studies have adopted qualitative approaches. This paper, on the other hand, presents a ratio-based approach for evaluating the benefits of KMSs. The proposed approach is tested using one of the KMSs from a study EC firm: the proposal preparation assistant (PPA) system. Based on a test of two real cases, this study found that a 48.7% time reduction in data collection and 25.3% staff-hour savings can be achieved when using the PPA system to prepare service proposal drafts. The...

Model of Proactive Problem-Solving for Construction Knowledge Management

Construction is an experience-based discipline, knowledge or experience accumulated from previous projects plays very important role in successful performance of new works. More and more construction organizations have adopted commercial Knowledge Management Systems (KMSs) for developing their own Knowledge Management (KM) functionalities. The existing KMS’s are mostly developed based on Communities of Practice (COPs) for knowledge sharing and exchange. Such approach founds on the reactive problem-solving (RPS) method. That is, the problem raised by the questioner in the COP has to passively “wait” for the members to respond (or react). Previous research indicates that such RPS approach may suffer in poor time and cost effectiveness. This paper proposes a proactive problem-solving (PPS) approach called Model of Proactive Problem-Solving (MPPS) for KMS. Unlike RPS, the PPS proactively solves the problem based on lessons-learned from previous projects. Should the solution is not available; the MPPS dispatches the problem to the most appropriate domain experts so that the problem can be tackled timely and efficiently. The MPPS is described in details, and an example construction KMS that adopts the proposed MPPS is demonstrated. It is found that the proposed MPPS has significant potentials to improve the performance of KMS for construction organizations.

A Pilot Study on Enhancing the Application of Knowledge Management Systems Using Semantic Segmentation

Owing to extensive knowledge management activities are promoted in various enterprises. How to enhance the application of knowledge management systems (KMS) becomes a critical issue. One of the enhanced applications of KMS is the SOS system, which aims at sharing knowledge for solving emerging problems. While it takes time for experienced employees to share their knowledge via the SOS system, it is advantageous to introduce an Automatic Problem Answering (APA) mechanism. When an emerging problem is issued, the APA mechanism will find actively suitable knowledge from the Intellectual Asset Repository (IAR), which consists of various sources of knowledge. In this paper, a semantic segmentation method is proposed and is employed in building a semantic segmentation module (SSM) in APA. SSM will automatically extract the knowledge corpuses embedded in documents. The extracted knowledge corpuses will be reused in APA to solve emergent problems and thus the application of KMS is enhanced.