The Development of a Knowledge based Expert System for the Selection of Earthmoving Equipment

2009 ◽  
Author(s):  
J. Christian ◽  
H. Caldera
Keyword(s):  
Expert System ◽  
Knowledge Based ◽  
Earthmoving Equipment ◽  
Selection Of

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.

Improving earthmoving estimating by more realistic knowledge

1996 ◽  
Vol 23 (1) ◽  
pp. 250-259 ◽  
Author(s):  
John Christian ◽  
Tian Xing Xie
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Human Experience ◽  
Critical Element ◽  
Engineering Project ◽  
Knowledge Based ◽  
Accurate Knowledge ◽  
The Common ◽  
The Cost ◽  
Selection Of

Earthmoving operations are often a very important aspect of a civil engineering project. Very accurate knowledge of the earthmoving operation is often the vital, critical element in the formation of a good and accurate cost estimate and schedule because of its prominent role in influencing costs and time. Inaccuracies are often built into earthmoving estimates by the fairly sweeping assumptions that are made during the estimating process. These problems were revealed in answers to a questionnaire and in interviews with experts. The factors that affect the performance of machines are discussed, including the common types of earthmoving operations. The importance of haul distance in determining which type of equipment should be used is also considered. The selection of equipment and estimation of costs for earthmoving depend heavily on human experience. The means of eliciting human experience are described in detail. Because of this reliance on human experience, a prototype knowledge-based expert system was developed using a shell program. The system is able to select the most appropriate fleet of machines, and estimate the cost, for use as a decision support system in planning an earthmoving operation. Key words: construction, earthmoving, estimating, knowledge, expert system, realistic knowledge.

Operator Workload Knowledge-Based Expert System Tool (OWLKNEST) Demonstration

1989 ◽  
Vol 33 (17) ◽  
pp. 1140-1141
Author(s):  
Regina M. Harris ◽  
Susan G. Hill ◽  
Robert J. Lysaght
Keyword(s):  
Expert System ◽  
System Development ◽  
Complete Information ◽  
Estimation Techniques ◽  
Knowledge Based ◽  
Development Cycle ◽  
Floppy Diskette ◽  
The Impact ◽  
Operator Workload ◽  
Selection Of

Objectives The Operator Workload Knowledge-based Expert System Tool (OWLKNEST) is a tool that provides guidance in selecting the most appropriate technique(s) for estimating or predicting Operator Workload (OWL). This demonstration will provide hands-on usage for interested parties in utilizing OWLKNEST to determine the most appropriate OWL technique for their particular situation, interpreting the resulting outputs, and performing sensitivity analysis to assess the impact of changing responses. Description of subject matter A variety of OWL estimation techniques are available, but information about these techniques and their appropriateness for particular situations may be difficult to assimilate. Furthermore, information on practical issues concerning the applicability of a particular tool is often unavailable—e.g., time, cost, personnel issues, etc. The selection of the appropriate technique(s) for assessing OWL is complex and beyond the scope of many practitioners. Therefore, an expert system approach was selected to provide a tool which can serve as a clearinghouse of knowledge as well as providing recommendations for workload estimation techniques. OWLKNEST requires an IBM PC type microcomputer equipped with a minimum of 640 Kb memory and two floppy diskette drives of at least 360Kb. Exsys Professional, a rule-based, backward-chaining system, is the expert system shell used for OWLKNEST. OWLKNEST incorporates an hierarchical logic structure that quickly focuses on the most applicable technique(s) and minimizes the number of questions posed by the system. It is based upon a taxonomy which divides OWL techniques into analytical and empirical techniques. OWLKNEST utilizes a question-and-answer dialogue to facilitates use by inexperienced analysts and is supplemented by embedded help features. The inputs are fed into the expert system, which applies rules and knowledge based on user information. The result is a suggested list of appropriate techniques. The user can obtain brief descriptions of the recommended technique(s) including references and descriptions of prior applications. OWLKNEST assumes that users have at least a fundamental knowledge of OWL concepts but can be novice computer and expert system users. The development of OWLKNEST was sponsored by the Army Research Institute and free copies of the runtime version of OWLKNEST are available to interested parties. Additional details about OWLKNEST are available in Hill and Harris, 1989 and Harris et al, 1989. More complete information about utilizing OWLKNEST can be obtained in the user's guide—Handbook for Operation of the OWLKNEST Tool (HOOT) (Harris et al., 1989). Importance The increasing criticality of performing OWL assessments throughout the system development cycle has Id to the recognition that a comprehensive, easy-to-use tool to recommend the appropriate OWL techniques is needed. OWLKNEST provides this capability and supplies information on the appropriate OWL techniques based on individual needs and resources. Possible Applications OWLKNEST will be useful in military application as well as other application areas where prediction and evaluation of operator workload is of interest. Similarly, the workload techniques are not application specific, but can be used in a variety of domains. OWLKNEST can provide the information and guidance of an appropriate selection of workload techniques for a broad range of applications.

scholarly journals A knowledge-based framework for construction methods selection

1993 ◽  
Vol 20 (2) ◽  
pp. 236-246 ◽  
Author(s):  
Alan D. Russell ◽  
Ibrahim Al-Hammad
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Key Words ◽  
Operational Definition ◽  
Decision Making Process ◽  
Knowledge Based ◽  
Construction Methods ◽  
Definition Of ◽  
System Prototype ◽  
Selection Of

This paper describes the ingredients of a knowledge-based framework for selection of construction methods. They include an operational definition of construction method, a conceptual model of the decision-making process, an explanation of how project context and construction methods may be represented for methods selection and analysis purposes, the range of criteria that need to be considered, and a representation of construction expertise. These ingredients are illustrated using a prototype expert system, called CMSA (Construction Methods Selection Assistant), to select a shoring system for cut-and-cover tunnelling. Key words: construction methods, decision-making, expert system, prototype.

scholarly journals KNOWLEDGE BASED EXPERT SYSTEM APPROACH TO INSTRUMENTATION SELECTION (INSEL)

Transport ◽  
2004 ◽  
Vol 19 (4) ◽  
pp. 171-176 ◽  
Author(s):  
Sudhikumar Barai ◽  
Padmesh Charan Pandey
Keyword(s):  
Expert System ◽  
Knowledge Engineering ◽  
Selection Process ◽  
Experiential Knowledge ◽  
Structural Domains ◽  
Instrument Selection ◽  
Knowledge Based ◽  
Recent Developments ◽  
Selection Of

The selection of appropriate instrumentation for any structural measurement of civil engineering structure is a complex task. Recent developments in Artificial Intelligence (AI) can help in an organized use of experiential knowledge available on instrumentation for laboratory and in‐situ measurement. Usually, the instrumentation decision is based on the experience and judgment of experimentalists. The heuristic knowledge available for different types of measurement is domain dependent and the information is scattered in varied knowledge sources. The knowledge engineering techniques can help in capturing the experiential knowledge. This paper demonstrates a prototype knowledge based system for INstrument SELection (INSEL) assistant where the experiential knowledge for various structural domains can be captured and utilized for making instrumentation decision. In particular, this Knowledge Based Expert System (KBES) encodes the heuristics on measurement and demonstrates the instrument selection process with reference to steel bridges. INSEL runs on a microcomputer and uses an INSIGHT 2+ environment.

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