A Coupled Knowledge-based Expert System for Design of Liquid Retaining Structures

This paper describes a coupled knowledge-based system for the design of liquid retaining structures, which can handle both the symbolic knowledge processing based on engineering heuristics in the preliminary synthesis stage and the extensive numerical crunching involved in the detailed analysis stage. The prototype system is developed by employing blackboard architecture and a commercial shell VISUAL RULE STUDIO. Its present scope covers design of three types of liquid retaining structures, namely, a rectangular shape with one compartment, a rectangular shape with two compartments and a circular shape. Through custom-built interactive graphical user interfaces, the user is directed throughout the design process, which includes preliminary design, load specification, model generation, finite element analysis, code compliance checking and member sizing optimization. It is also integrated with various relational databases that provide the system with sectional properties, moment and shear coefficients and final member details. This system can act as a consultant to assist novice designers in the design of liquid retaining structures with increase in efficiency and optimization of design output and automated record keeping. The design of a typical example of the liquid retaining structure is also illustrated. Introduction Crack width control is imperative in liquid retaining structures, which are exposed to a corrosive environment. The design of these structures is quite specialized and requires assimilation of knowledge from heuristics, research findings and standard engineering methodology. It requires extensive studies of loading requirements, underground geotechnical conditions, wind force characteristics, load combinations, practical dimensions and distribution of reinforcement and concrete, material properties and the exposure environment. Moreover, deviations often exist between the assumed properties of components at the preliminary design stage and that at the detailed design stage. In such cases, re-analysis will be entailed and iterative steps such as numerical modeling, structural analysis and code conformance checking are usually involved. Existing computer models may be available to perform a particular task in the whole design process. It is difficult to code empirical rules or expert knowledge in a conventional algorithmic framework. The application of these individual programs requires the intensive knowledge of the structural designer and they are prone to human errors during the data transferring processes. Recent advances in artificial intelligence techniques have rendered it possible to incorporate the heuristic knowledge into the conventional algorithmic structural analysis models. A knowledge-based system (KBS) is an interactive decision-making computer tool that mimics the reasoning processes of experts in a problem domain. Basic components of a KBS are system context, knowledge base, inference engine, knowledge acquisition, a user interface and explanation facilities. During the last decade, KBSs have been widely adopted to solve problems in many different disciplines (Chau, 1992; Chau and Anson, 2002; Chau This is the Pre-Published Version.