The development of an environmental noise decision support system

• Main Author: Ibrahim, Hatem Galal Abdel-Azim
• Published: University of Liverpool 2000
• Subjects: 693.834028563
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399063

The methodology of noise control exists to solve many noise problems. The most cost effective methods of controlling noise are those that can employed in advance to prevent any potential noise problem occurring. The majority of architects are overlooking to consider any noise problem during the process of the site planning. This is because the existing methods need long procedure to arrive at the required conclusion regarding the noise performance of the site. The existing methods depend of predicting the noise level at the reception point that effected by the noise source. This depends on many variables, including the distance from source; the propagation effects include screening and ground attenuation ...etc. The second stage comes to establish the kind of the building that the receiver will use. At this stage, the noise control expert can establish the required noise performance to solve the problem. Another complicated procedure comes by using the methodology of the noise control, which branches to many options. The chosen option depends on a certain priority and the condition of the architect. On the basis of the above, the thesis concerns with developing another method using the existing techniques, but to use by the architects or the novice users. This method gives the prediction of the noise level at the reception point, establishing the required noise performance and finally gives the suitable advice to solve the problem. The Artificial Intelligence (AI) techniques in general and Expert System (ES) in particular have been employed to develop this method. The most important part in this technique is the knowledge base that will be used to fulfil the desired objective This knowledge has to proceed within many stages, which called knowledge acquisition process. This process consists of 5 main stages, which concerns with identifying the objectives of the system with drawing a relationship between the different factors that effect the desired conclusion. This knowledge should be taken to establish the main concept that will be used to be represented in an expert system. The next stage comes to formalise the collected knowledge followed by the implementation stage and finally the evaluation of the system.