A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit <a rel="external noopener" href="https://bjrbe-journals.rtu.lv/article/download/bjrbe.2019-14.433/1538">the original URL</a>. The file type is <code>application/pdf</code>.
<i title="Riga Technical University">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/y26vsbh5mvaynfi55flxoeisaa" style="color: black;">Baltic Journal of Road and Bridge Engineering</a>
Artificial Neural Networks represent useful tools for several engineering issues. Although they were adopted in several pavement-engineering problems for performance evaluation, their application on pavement structural performance evaluation appears to be remarkable. It is conceivable that defining a proper Artificial Neural Network for estimating structural performance in asphalt pavements from measurements performed through quick and economic surveys produces significant savings for road<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7250/bjrbe.2019-14.433">doi:10.7250/bjrbe.2019-14.433</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a5hveknoijf63joeb6wrns2nku">fatcat:a5hveknoijf63joeb6wrns2nku</a> </span>
more »... ies and improves maintenance planning. However, the architecture of such an Artificial Neural Network must be optimised, to improve the final accuracy and provide a reliable technique for enriching decision-making tools. In this paper, the influence on the final quality of different features conditioning the network architecture has been examined, for maximising the resulting quality and, consequently, the final benefits of the methodology. In particular, input factor quality (structural, traffic, climatic), "homogeneity" of training data records and the actual net topology have been investigated. Finally, these results further prove the approach efficiency, for improving Pavement Management Systems and reducing deflection survey frequency, with remarkable savings for road agencies.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190429052906/https://bjrbe-journals.rtu.lv/article/download/bjrbe.2019-14.433/1538" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b4/1d/b41d29b3d4a018b89d130ede2b57e517203a5a31.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7250/bjrbe.2019-14.433"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>