A growing interest in security and occupant exposure to contaminants revealed a need for fast and reliable identification of contaminant sources during incidental situations. To determine potential contaminant source positions in outdoor environments, current state-of-the-art modeling methods use computational fluid dynamic simulations on parallel processors. In indoor environments, current tools match accidental contaminant distributions with cases from precomputed databases of possible concentration distributions. These methods require intensive computations in pre-and postprocessing. On the other hand, neural networks emerged as a tool for rapid concentration forecasting of outdoor environmental contaminants such as nitrogen oxides or sulfur dioxide. All of these modeling methods depend on the type of sensors used for real-time measurements of contaminant concentrations. A review of the existing sensor technologies revealed that no perfect sensor exists, but intensity of work in this area provides promising results in the near future. The main goal of the presented research study was to extend neural network modeling from the outdoor to the indoor identification of source positions, making this technology applicable to building indoor environments. The developed neural network Locator of Contaminant Sources was also used to optimize number and allocation of contaminant concentration sensors for real-time prediction of indoor contaminant source positions. Such prediction should take place within seconds after receiving real-time contaminant concentration sensor data. For the purpose of neural network training, a multizone program provided distributions of contaminant concentrations for known source positions throughout a test building. Trained networks had an output indicating contaminant source positions based on measured concentrations in different building zones. A validation case based on a real building layout and experimental data demonstrated the ability of this method to identify contaminant source positions. Future research intentions are focused on integration with real sensor networks and model improvements for much more complicated contamination scenarios.
Information flows in construction projects are generally focussed on the needs of the design and construction phases. This creates disruption of workflows across the project stages and in particular with the information handover to the operation stage. The adherence to client requirements for the operation phase of buildings becomes very challenging. A structured information delivery enabled by BIM protocols, established at the project's inception phase, can help: 1. prevent information loss during the project development; 2. ensure the coordinated delivery of the clients' requirements as stated at the pre-design stage, and 3. anticipate the impact of client decisions at early project stages on the operational performance of buildings. This research presents a methodology and a decision support system to help obtaining, categorizing and trading off sustainability and facility management values using subjective driven priorities from top-level management. The decision support system will assist, within digitally enabled projects, in translating these priorities into objective parameters and information categories. These can be subsequently included within the project tender and bidders' BIM Execution Plans. The tool will also help to monitor the performance of the project design with the national sustainability and the client targets as the project progresses. The proposed tool is presented within the context of Qatar but it could be applied in other countries.
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