Expert Systems and Database Systems

Expert systems (ES) and database systems (DBS) are major components of information systems and important assets to companies. The development of these systems represent users' knowledge in the application systems. As computer technologies evolve, and as users requirements change, there is a need to upgrade these system to meet the new application requirements. To preserve the knowledge of the existing information systems, a methodology for integrating ES and DBS into an expert database system (EDS) is proposed. The integrated EDS is a knowledge based system (KBS) which derives and stores knowledge in a frame model consisting of a class header, attributes, methods and constraints. It extracts the ES rules and DBS data for an application into coupling classes at run time only. The attributes of the coupling classes are matched with synonyms in a synonym table which resolves their naming and semantic conflicts with user assistance in knowledge acquisition. The resultant EDS is a KBS ready for application development.

The scope of the book is the application of vibration mitigation systems in structural engineering. The intended content includes the theoretical background covering aspects from both structural dynamics and control engineering point of view. Moreover, passive, active and semi-active devices are explained in detail giving mathematical principles, design considerations and application examples. It also contains detailed information about structural monitoring, as an essential part of the active/semi-active systems, and therefore, provide a full overview about passive, active and semi-active systems in the specific context of civil engineering Book presents a comprehensive coverage of the area of vibration control of civil structures subjected to different types of loading while using passive, semi-active, and/or active controls. Presents the theoretical governing equations as well as the associated design guides of various vibration control mitigation approaches. Discusses structural monitoring aspects such as sensor technology, system identification and signal processing topics. Reviews structural control aspects, such as algorithms. Includes solved examples utilizing MATLAB®/SIMULINK® with source codes of the calculation examples and design tool set. This book is aimed at graduate students, professionals, researchers in civil engineering, structural engineering, structural dynamics, health monitoring, vibration control.

This introductory book presents procedures for the analysis of signals and the characterization of systems in civil engineering. The two main topics of this book are at the core of several fast-growing areas in civil engineering today, including: non-destructive testing, system identification, image processing, local or remote monitoring, smart structures, dynamic loading, and feedback control systems in transportation and structural engineering. The authors have provided the readers with a Web site dedicated to this book which includes readily downloadable algorithms for numerical examples, other examples and applications, and errata. Complete with end-of-chapter exercises, this book is appropriate as an upper undergraduate/graduate level textbook or as a reference source for the research or practicing engineer.

Object oriented paradigms for integrated structural engineering

The use of expert systems in civil engineering and especially in road construction has become a necessity due to the structure’s specificity in dimensioning, which is completely different from other structures. The parameters involved in road structure dimensioning are multidisciplinary in their values and behaviors; the road structure is complex and composed of heterogeneous materials, and its behavior is unpredictable due to the nature of the soil, the influence of climate, and different loads it supports. This expert system proposes different thickness, stress, and strain solutions by comparing them with the allowable values based on different mechanical models and formulates the methods and data sheets and then provides choices of body composition for the road based on the new version of the Algerian pavement catalog (2003). This tool is organized in the following forms: (1) rules derived from the experiences of professionals in the field. For our case, these rules are drawn from one side on the catalog of new pavements (version 2003) and another on a set of theoretical and practical method. (2) The property of GURU to call external programs of course that it is compatible. This property will help us to automate some models of flexible pavement sizing “Boussinesq Model, Westgaard Model, Odemark Model” with the Dev C++ language “which is a recent version of C”. (3) The Algerian method outlined in the catalog.

An expert database system is an expert system which is developed on databases using DBMS technology to manage facts and rules. Frame-based expert systems are widely used as the knowledge representation for such expert systems with large knowledge bases. Many systems have the ability to connect to external databases. Facts stored in databases can be loaded into expert system's knowledge base and inference is performed by the inference engine of the expert system. In many cases, such external facts are required several times for each inference. Thus, a lot of communication traffic takes place. This research work present the design and implementation of a frame-based object-relational database system which has a tight coupling between the expert system and the external knowledge base. The external knowledge base also use frame as its knowledge representation. More over, it has its own inference engine so that inference can be perform on the knowledge base side and the results, not only simple facts, are sent back to the expert system for further inference. A medical consultation system is used as an illustrated example an Oracle lOg object relational DBMS is used as the database platform.

A review study of how adaptive ‘reuse’ and ‘reduce’ will help to bring the sustainable environmental conditions for the current and future generation from the civil engineering—stability and safety point of view, is attempted in this paper. Further using both these different approaches, the knowledge of carbon footprint, embodied energy of the material that forms the core of the structure are understood and judiciously used for its design. The type of methods which are attempted to bring this adaptive reuse and reduce principles of sustainability in the real-life situation through the case study are reviewed. The structural health monitoring system is used to ascertain the standards and procedures that can also help to achieve sustainability to a substantial level is identified. The various types of the structural monitoring system and the various types of algorithms followed to bring the necessary study data for analysis are understood from various papers written already. The use of structural monitoring systems can also facilitate in averting the structural adversity in the infrastructure and buildings. It is understood that the future of civil engineering will have the guidelines and procedures in integrating SHM into the mainstream practice, thereby achieving the requisite sustainable principles for a promising future.KeywordsAdaptive reuseReduceLife cycle analysisStructural monitoringSustainability

In Memoriam: Dr. Clive L. Dym (1942–2016)

In all times of history, engineers and architects have searched for opportunities to develop adaptive structures, buildings and building parts, which are equipped for adjusting to ever-changing requirements and conditions. The reasons behind this interest relate to the growing need for functional/ spatial flexibility, sustainability and extended capabilities of structural performance. Recent advancements in construction technology, robotics, architectural computing and material science have increased the interest for these structures/ systems; and allowed us to develop examples that are more advanced. This paper aims to introduce the state of art contemporary adaptive systems in architecture and structural engineering; and presents a future perspective for these systems and their potential applications in the construction industry.

(1989). Knowledge based systems in civil engineering. Civil Engineering Systems: Vol. 6, No. 1-2, pp. 3-4.

Expert-system technology has emerged from beneath the umbrella of Artificial Intelligence and into the mainstream of engineering problem solving. There already are several notable examples of the application of this state-of-the-art technology in the petroleum industry. Clearly, expert systems that run efficiently on standard personal computers are particularly of value. A new expert-system generator that overcomes the speed, memory and mass-storage limitations of XT/AT-class personal computers is presented in this paper. The shell is optimized for the specific characteristics of the target user, knowledge expert and computer. Expert systems developed using this shell currently are being used by our technical personnel for drilling fluid and related applications.

A new system architecture shares certain characteristics with database systems, expert systems, functional programming languages, and spreadsheet systems, but is very different from any of these.

Modeling of forced-vibration systems using continuous-time state-space neural network

Expert systems have now proven their value in a variety of enterprises ranging from medicine to geology. Small expert systems are at work in numerous commercial and industrial sites, but large systems are still rare. Besides the difficulties inherent in the collection of useful and correct domain knowledge, large knowledge bases present special problems in data management. These problems often cause a good pilot system to founder when expanded to an operational size.This paper addresses the difficulties of efficiently handling large knowledge bases. The methods discussed result from viewing a knowledge base as a database and analyzing the knowledge as one would analyze a database application, using Chen's Entity-Relationship (E-R) Diagrams [CHEN76] to build a relational database representation. Besides providing a different perspective on the management of knowledge bases, such a method facilitates the use of a commercial DBMS for this task.Following these ideas the simple assertions of the knowledge base are viewed as one entity type and the rules themselves as another, with IF and THEN being separate relationships between these two types. This decomposition is reasonable for capturing the knowledge represented by the rules and leads to a relational description which is considerably different than usual rule representations. More precisely, the E-R diagram of Figure 1 is taken as the basis of the model, where the rectangles represent entities and the ellipses represent relationships between the entities connected to that ellipse. The unboxed words such as rule# are attributes of the associated entities and correspond to the information to be saved in the database.The E-R diagram in Figure 1 can be converted to the relational database tables shown in Figure 2 where rule# is a name or a number that identifies the rule, if_part is a simple assertion Ai (antecedent), then_part is a simple assertion Ci (conclusion), and conf is a number representing the confidence in that deduction.This results in a very different data organization from the usual collection of knowledge found in rule-based systems. Beyond that analysis, very specific implementation techniques are suggested for knowledge bases, using both standard relational data bases and simpler file-handling techniques such as those available in standard programming languages. Caution must be taken, however, before simply trying to turn knowledge base management over to a commercial DBMS, since the behavior of “queries” within a rule base is very different from that of ordinary database queries. Comparisons are made to such recent works as those of Deering, Debanne, and Bouzegoub which suggest a variety of implementations for knowledge bases.Often in the modeling of a domain, one discovers a hierarchical relation among the basic concepts, vocabulary, or objects of study. Such an IsA or AKindOf hierarchy can be used to create a segmentation of the knowledge base resulting in significant efficiency improvements within the system. This possibility is explored in some detail.Throughout this work, emphasis is given to dynamic rule bases which are subject to frequent changes and which, therefore, require a generalized inference engine and flexible data structures. Often due to execution speed requirements some domain knowledge is encoded as procedures within the inference engine. Although this may improve the efficiency of the system, it undermines the generality and flexibility that are also required. This paper investigates a promising method for incorporating such procedural inclusions within the general knowledge base structure. The approach developed allows the absorption of changes without restructuring of the knowledge base or modifications to the inference engine, and maintains system transparency, simplicity, and expandability.

Advances of FRP-based smart components and structures