Development of Inspection System for Crack in R.C. Tunnel Lining by Using Knowledge-Based System (KBS)

2012 ◽  
Vol 622-623 ◽  
pp. 1415-1420
Author(s):  
Yousif A. Mansoor ◽  
Zhi Qiang Zhang
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Visual Inspection ◽  
Tunnel Lining ◽  
Inspection System ◽  
Knowledge Based Systems ◽  
System Environment ◽  
Periodic Inspection ◽  
Knowledge Based ◽  
Satisfactory Answer

Over the last several years, many concrete tunnels have been constructed for roads, highways, and railways. For safety in concrete tunnel, periodic inspection has been conducted using many testing technologies and techniques. However, these technologies cannot replace visual inspection because of their slow and complicated procedures. For this reason, the Knowledge-Based Systems (KBS) are used to diagnose R.C tunnel lining crack damage (DICRCTL). In this paper, we attempt to propose an alternative to the human expert, to give technical decisions in diagnosing crack damages in second segment of R.C. tunnel lining. To overcome this requirement, an expert system is developed to achieve the research aim. This proposed system was constructed on a knowledge base that incorporates with the gathered information in the form of rules which is suitable to implement in an expert system environment to diagnostic advisory nature. The proposed application results show an easy, fast and satisfactory answer to engineering needs.

OPGEN: A System to Identify and Evaluate Potential Applications of Knowledge Based Systems

1992 ◽  
Author(s):  
Samir Rohatgi ◽  
James H. Oliver ◽  
Stuart S. Chen
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Knowledge Based Systems ◽  
The Core ◽  
Knowledge Based ◽  
Evaluation Functions ◽  
Potential Applications ◽  
Computer Based ◽  
Suitable System ◽  
Overall Effectiveness

Abstract This paper describes the development of OPGEN (Opportunity Generator), a computer based system to help identify areas where a knowledge based system (KBS) might be beneficial, and to evaluate whether a suitable system could be developed in that area. The core of the system is a knowledge base used to carry out the identification and evaluation functions. Ancillary functions serve to introduce and demonstrate KBS technology to enhance the overall effectiveness of the system. All aspects of the development, from knowledge acquisition through to testing are presented in this paper.

Knowledge-based systems and operational hydrology

1991 ◽  
Vol 18 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Slobodan P. Simonovic
Keyword(s):  
Expert System ◽  
Flow Measurement ◽  
Knowledge Based Systems ◽  
Flow Measurements ◽  
Knowledge Based ◽  
Original Concept ◽  
System Water ◽  
Potential Use ◽  
Selection Of

Knowledge-based systems were brought to the attention of hydrologists almost a decade ago. The application of knowledge-based systems technology is natural and appropriate for the field of hydrology because it contains numerous procedures developed from theory, actual practice, and experience. The emphasis of the present paper is on demystifying knowledge-based systems of artificial intelligence. After a detailed review of the most important applications to the field of hydrology, the original concept for applying knowledge-based technology is presented. The discussion ends with the list of possible benefits from the application of knowledge-based technology. An expert system for the selection of a suitable method for flow measurement in open channels is used as a case study to illustrate the discussion in the paper. The system has been designed for potential use in Environment Canada. Key words: expert system, water resources, hydrology, flow measurements.

Diagnosis Specialist System for managing Palm Oil Plantation Problems

2019 ◽  
Vol 1 (2) ◽  
pp. 113-118
Author(s):  
Norhasniza Hassan ◽  
Zalmiyah Zakaria ◽  
Zuraini Ali Shah ◽  
Shahreen Kasim
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Palm Oil ◽  
Data Driven ◽  
Computerize System ◽  
Related Field ◽  
Tree Disease ◽  
Knowledge Based ◽  
System Concept ◽  
Problem Data

 The project that was developed is a computerize system that will be ableto diagnose problems happened to palm oil tree. Generally, there is twomain problem that always happened to palm oil tree; disease and pest.This system developed to store the knowledge in the related field into aknowledge base to help the newcomer in this field to make a decision tosettle the problem faced. To solve this problem, data driven searchtechnique applied to search for the symptom stored in the knowledgebase. The system developed using expert system concept. Based on thisconcept, a knowledge base was built to store all the knowledge compiled.Inference engine used to search the knowledge in the knowledge base.The knowledge based was stored in MySQL database and the inferenceengine was developed using PHP language.

MENUDA: A Knowledge-Based Menu Design Expert System

1987 ◽  
Vol 31 (12) ◽  
pp. 1315-1319
Author(s):  
Kuocheng A. Parng ◽  
Vernon S. Ellingstad
Keyword(s):  
User Interface ◽  
Expert System ◽  
Knowledge Base ◽  
Interface Design ◽  
Design Guidelines ◽  
Current Version ◽  
Design Expert ◽  
Knowledge Based ◽  
Menu System ◽  
Menu Design

An experimental knowledge-based menu design assistant (MENUDA) was developed to aid the design of menu systems. A conceptual model was first developed to provide a structured construct to organize knowledge of menu system design from the available literature, and to serve as a paradigm for the development of the MENUDA system. The knowledge base and the user interface of the MENUDA system were developed under an interactive microcomputer environment supported by Texas Instruments' Personal Consultant Plus. The current version of the MENUDA system is described in the paper. In addition, the methodology used to derive rules in the MENUDA knowledge base and the appropriateness of employing a knowledge-based expert system approach to providing user interface design guidelines are discussed.

Knowledge Extraction from Domain-Specific Documents

2017 ◽  
pp. 106-123
Author(s):  
Ram Kumar ◽  
Shailesh Jaloree ◽  
R. S. Thakur
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Knowledge Bases ◽  
Domain Ontology ◽  
Knowledge Based Systems ◽  
Domain Specific ◽  
Knowledge Based ◽  
Representation Systems ◽  
Terminological Confusion ◽  
Knowledge Representation Systems

Knowledge-based systems have become widespread in modern years. Knowledge-base developers need to be able to share and reuse knowledge bases that they build. As a result, interoperability among different knowledge-representation systems is essential. Domain ontology seeks to reduce conceptual and terminological confusion among users who need to share various kind of information. This paper shows how these structures make it possible to bridge the gap between standard objects and Knowledge-based Systems.

scholarly journals Knowledge-Based Systems Manufacturing With Viability Ensuring

2021 ◽  
Author(s):  
Valeriya V. Gribova ◽  
Elena A. Shalfeeva
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
Product Family ◽  
Knowledge Bases ◽  
Knowledge Based Systems ◽  
Manufacturing Environment ◽  
Development Environment ◽  
Systematic Analysis ◽  
Knowledge Based ◽  
Medical Software

Abstract With highly increased competition, intelligent product manufacturing based on interpretable knowledge bases has been recognized as an effective method for building applications of explainable Artificial Intelligence that is the hottest topic in the field of Artificial Intelligence. The success of product family directly depends on how effective the viability mechanisms are laid down in its design. In this paper, a systematic cloud-based set of tool family is proposed to develop viable knowledge-based systems. For productive participation of domain and cognitive specialists in manufacturing, the knowledge base should be declarative, testable and integratable with other architectural components. Mechanisms to ensure KBS viability are provided in an ontology-oriented development environment, where each component is formed in terms of domain ontology by using the adaptable instrumental support. Due to the explicit separation of ontology from knowledge, it became possible to divide competencies between specialists creating an ontology and specialists creating a knowledge base. We rely on the fact that the activity of creating an ontology is significantly different from the activity of creating a knowledge base. Creating an ontology is a creative process that requires a systematic analysis of the domain area in order to identify common patterns among its knowledge.The characteristic properties of knowledge-based systems related to viability are described. It is explained, how these properties are provided in development environments implemented on cloud platform. The concept of a specialized manufacturing environment for knowledge-based system is introduced. The necessary set of tools for such ontology-oriented environment construction is determined. The example of tools for creating specialized manufacturing environments is the instruments implemented on the «IACPaaS» platform. The IACPaaS is already used for collective development of thematic cloud knowledge portals with viable knowledge-based systems. This specialized manufacturing environment has enabled the creation of multi-purpose medical software services to support specialist solutions based on knowledge being remotely improved by experts.

Rapid development of knowledge-based systems via integrated knowledge acquisition

2003 ◽  
Vol 17 (3) ◽  
pp. 221-234 ◽  
Author(s):  
HAO XING ◽  
SAMUEL H. HUANG ◽  
J. SHI
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Traditional Approach ◽  
Rapid Development ◽  
Real Life ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Linguistic Rules

This paper presents a novel approach, which is based on integrated (automatic/interactive) knowledge acquisition, to rapidly develop knowledge-based systems. Linguistic rules compatible with heuristic expert knowledge are used to construct the knowledge base. A fuzzy inference mechanism is used to query the knowledge base for problem solving. Compared with the traditional interview-based knowledge acquisition, our approach is more flexible and requires a shorter development cycle. The traditional approach requires several rounds of interviews (both structured and unstructured). However, our method involves an optional initial interview, followed by data collection, automatic rule generation, and an optional final interview/rule verification process. The effectiveness of our approach is demonstrated through a benchmark case study and a real-life manufacturing application.

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