Page 87 - Built Expressions - Online Construction Magazine - November 2014 Issue

bangordailynews.com hycos.com Hulk Mechanised To achieve reasonable proft, contractors try to minimize equipment requirements. Unfortunately the cost of using equipment especially cranes on site. this information is incomplete and generally Normally they prefer to use the smallest size crane requires the user to make bold decisions on job conditions and categories of cranes for a capable of completing the task. However, contractors rely particular situation leading to unavoidable on their in-house professional advice concerning the type mistakes and perhaps to costly decisions. of the crane to be used. Selecting a crane, requires prediction as to the consequences of the choice that is to be made. A wrong decision is likely to have signifcant effects in terms of high cost and possible delays. The ability to predict and experience gained during many years of work on construction sites. Most make decisions grows out of knowledge and of the time this knowledge is not available to the decision maker when needed. This makes the knowledge-based systems valuable tools to be used as decision supporting system during the crane selection process. Conventional algorithmic programs are unable to manipulate heuristic and qualitative knowledge necessary for the equipment selection. On the other hand Knowledge Based Expert System (KBES) are not robust in numerical data manipulation, while being very effective in declarative knowledge manipulation and handling of logical inferences and reasoning. Therefore, expert systems and conventional programming can be combined to support effective decisions for crane selection. With this respect an integrated computer system called capable of advising the users on the selection of appropriate cranes has been developed. Experts knowledge has been captured, classifed and coded in the system’s knowledge-base. The system integrates a knowledge-base with algorithmic programs, and commercially available tools such as: database management, spreadsheet applications, graphics and simulations. The system incorporates two main modules. The frst being a Knowledge-Based module that contains experts knowledge, heuristics and rules of thumb related to cranes selection. The second is a Case- Based module containing information on various cases representing already constructed buildings with pre selected crane(s). In addition to the knowledge-base, the system integrates procedural algorithms for performing routine calculations and graphics to support the crane selection process, in three other modules: geometry calculations, graphical validation, and cost estimation. All these modules share a global database containing information on a number of already constructed buildings, information on problems related to cranes used in their construction, and data on a large number of commercially available cranes, that includes their types, specifcations and costs. LEVEL 5 as an Object-Oriented Expert System shell, has been used to develop the system [1]. The system allows for the stored data and knowledge to be accessed by all parties involved in the crane selection process. Vol: 3 Issue: 11ol: 3 Issue: 11 No Built Expressions PG87 November 2014vember 2014 V