The development of an environmental noise decision support system
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.