A firefighter protective clothing consists often of three-layered fabric structure; an outer shell, a moisture barrier and a thermal liner. In this study, an innovative firefighter protective jacket is proposed, designed to protect firefighters within the thermal environment. The protective performances of different three-layered fabrics were initially tested, and the most appropriate fabric combination for firefighter protective clothing was determined. After the fabric selection process, a firefighter jacket was designed and produced by using the most appropriate fabric combination. A specially designed electronic system equipped with related sensors was integrated to the jacket. Finally, the designed firefighter jacket with integrated sensors was tested in a heating oven.
The firefighter protective clothing is comprised of three main layers; an outer shell, a moisture barrier and a thermal liner. This three-layered fabric structure provides protection against the fire and extremely hot environments. Various parameters such as fabric construction, weight, warp/weft count, warp/weft density, thickness, water vapour resistance of the fabric layers have effect on the protective performance as heat transfer through the firefighter clothing. In this study, it is aimed to examine the predictability of the heat transfer index of three-layered fabrics, as function of the fabric parameters using artificial neural networks. Therefore, 64 different three layered-fabric assembly combinations of the firefighter clothing were obtained and the convective heat transfer (HTI) and radiant heat transfer (RHTI) through the fabric combinations were measured in a laboratory. Six multilayer perceptron neural networks (MLPNN) each with a single hidden layer and the same 12 input data were constructed to predict the convective heat transfer performance and the radiant heat transfer performance of three-layered fabrics separately. The networks 1 to 4 were trained to predict HTI12, HTI24, RHTI12, and RHTI24, respectively, while networks 5 and 6 had two outputs, HTI12 and HTI24, and RHTI12 and RHTI24, respectively. Each system indicates a good correlation between the predicted values and the experimental values. The results demonstrate that the proposed MLPNNs are able to predict the convective heat transfer and the radiant heat transfer effectively. However, the neural network with two outputs has slightly better prediction performance
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