Authored by Osvaldo Cairó Battistutti*
Abstract
Expert systems are designed to solve complex problems by reasoning with and about specialized knowledge like an expert. The design of concrete is a complex task that requires expert skills and knowledge. Even when given the proportions of the ingredients used, predicting the exact behavior of concrete is not a trivial task, even for experts, because other factors that are hard to control or foresee also exert some influence over the final properties of the material. This paper presents some of our attempts to build a new expert system that can design different types of concrete (hydraulic, bacterial, cellular, lightweight, high-strength, architectural, etc.) for different environments. The system also optimizes the use of additives and cement, which are the most expensive raw materials used in the manufacture of concrete..
Keywords: Expert systems; Knowledge modeling; Knowledge acquisition; Design of concrete; Industrial applications
Introduction
By the middle of the last century, taking advantage of the rise of computing and the emergence of artificial intelligence (AI), people began to think about solving complex problems that required knowledge, intelligence and reasoning, using the computer. The possibility of including thought and reasoning in a machine woke up the euphoria of the researchers of that time. One of the fields of AI that relates thinking, logic, reasoning and knowledge, is that of expert systems (for design, diagnosis, planning, etc.). Expert systems are designed to solve complex problems by reasoning with and about specialized knowledge, like an expert. Clearly, expert systems are the most mature and widely used commercial application coming out of artificial intelligence. Thousands of expert systems have been developed worldwide and applied to different knowledge domains, such as finance, manufacturing, industries, management, airline scheduling, customer services, and military design [1-3]. Expert systems in industry are interesting because they offer four major advantages with respect to a human expert: a) increased distribution of expertise. They allow having any number of experts (copies of the software) in any place at the same time; b) longevity. Their lifetime is practically infinite; c) objectivity. They are never influenced by environmental factors that may hinder their decision-making capabilities, such as hasty decisions, emotions, cost of material, competitive factors, etc.; and e) cost. They can be used 24 hours a day, 365 days a year at no additional cost. On the other hand, the main disadvantage of these knowledge-based systems is the well-known knowledge engineering bottleneck: the knowledge acquisition process, which implies the transformation of tacit knowledge—which resides in the heads of experts—into explicit knowledge. To solve this major problem, which is also key to this project, we use the KAMET II Methodology of Cairó & Guardati [4]. This methodology represents a modern approach to creating diagnosis-specialized knowledge models that can be run on Protégé 2000, the open source ontology editor and knowledgebased framework; for details see Noy et al. [5].
This paper presents some of our attempts to build a new expert system that brings together and includes all of these ideas. Aspdin-named in honor of Joseph Aspdin, who was a British cement manufacturer who obtained the first patent for Portland cement in 1824-is an expert system that focuses on the intelligent design of different types of concrete: hydraulic, bacterial, cellular lightweight, high strength, architectural, etc. The system also optimizes the use of additives and cement, which, besides being the most expensive raw materials used in the manufacture of concrete, when employed in certain types of concrete add a lot of complexity to the decisionmaking process in terms of achieving the desired workability, mechanical strength, and durability (permeability, weathering, shrinkage, cracking, etc.). The quality of concrete depends precisely on the proportions of cement, additives, water, and air, considering the type of concrete that is being designed and the environment in which it will be used, see Arioz et al. [6]. In Mexico, companies like Cemex, Apasco, Cruz Azul, and Moctezuma have hundreds of manufacturing plants that each day produce about 1,000 cubic meters of concrete, where each cubic meter weighs about 2,400 kg. The per capita consumption in emerging countries is currently 400 kg per person per year. To illustrate the large size of cement production in these companies, recently Cemex, which is among the three major world producers of cement (1: Lafarge, 2: Holcim, 3: Cemex, 4: Heidelberg Cement, 5: Italcementi), signed various agreements to: a) provide 27,000 m3 of ready-mix concrete for the Airbus-350 long-range, wide-body, jet airliner assembly facilities near the southwestern city of Toulouse, France; b) provide over 30,000 m³ of different types of specialty ready-mix concrete for a bridge which crosses the Llobregat River near the Barcelona airport in Spain, c) provide 40,000m³ of specialty ready-mix concrete for the construction of the Baluarte Bicentennial Bridge in the Northern Mexican states of Sinaloa and Durango (Baluarte is one of the world’s tallest, 390mts, and most complex bridge mega-structures); d) supply 48,000 cubic meters of ready-mix concrete for the new construction of the Kaiserschleuse Bremerhaven, in Germany, one of the largest lock projects in Europe; e) provide 250,000 cubic yards of ready-mix concrete for the more than US$1 billion Port of Miami project in south Florida; and f) supply over 500,000 tons of cement for a major expansion of the Panama Canal. An expert system for the intelligent design of concrete would undoubtedly be a valuable tool for world-class companies like this. Aspdin can design different types of concrete for different environments, compare the results with those obtained by human experts, show the reasoning followed to reach the solutions it produces, and answer questions justifying its reasoning and conclusions.
The following section of this paper describes the KAMET II methodology used in Aspdin to model the knowledge acquired from multiple knowledge sources. Section 3 discusses the design of concrete and why this is such a difficult task. Section 4 is a description of the Aspdin system, its implementation, and architecture. Section 5 presents an analysis of how Aspdin could evolve from an academic prototype to be used in a beneficial manner in an industrial setting. Section 6 presents some conclusions about the project.
The KAMET II Methodology
The knowledge acquisition (KA) process does not involve mining from the expert’s head and writing rules for building knowledge-based systems (KBS), as it was considered twenty years ago when KA was often confused with knowledge elicitation, and modern engineering tools did not exist. The KA process has changed since then. It should be considered a cognitive process that involves both dynamic modeling and knowledge generation activities. KA should be seen as a spiral of epistemological and ontological content that grows upward by transforming tacit knowledge into explicit knowledge, which in turn becomes the basis for a new spiral of knowledge generation; for details see Cairó & Guardati [4]. Knowledge undoubtedly is a fluid mix of framed experience, values, expertise, and insight that can be useful and applied to solve a problem. However, we have a well-known problem (the design of concrete) which on the other hand is extremely difficult to solve. Knowledge is often tacit, residing in the minds of individuals, and therefore difficult to make explicit. That is the reason we use the KAMET II methodology. It represents a modern approach to building diagnosis-specialized knowledge models that can be run with Protégé-2000, the open source ontology editor and knowledge-based framework [5].
The KAMET II life-cycle model (LCM), shown schematically in Figure 1, provides a graphical framework for managing the knowledge acquisition process. The graphical framework also helps to set up and facilitate ways to characterize and organize knowledge acquired from multiple knowledge sources, share knowledge, implement the required actions, review the project situation, identify risks when objectives are not reached, monitor project progress, and check quality control; for details see Cairó & Guardati [4]. In fact, KAMET II is based on the search for the efficient transformation of tacit knowledge into explicit knowledge, see Sun et al. [7]. The KAMET II life cycle consists of four welldefined stages: the strategic planning of the project, initial model building, feedback model building, and final model building. Each stage involves a process of knowledge transformation (Figure 1).
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