Namo Research Articles

Connection stiffness reduction analysis in steel bridge via deep CNN and modal experimental data

This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.

Lith. namõ, OCS doma and the PIE directive

The termination of the isolated adverbs Lith. namo “homewards” and Sl. *dȍma “at home” is traced back to a common Balto-Slavic prototype *-ā, which in turn is argued to go back to a post-Anatolian variant *-oh2a of the PIE directive. The ending *-oh2a was formed through recharacterization of the inherited thematic directive *-oh2 (apocopated from pre-PIE *-o-h2e) with the athematic ending *-h2a. It yielded *-ā in Balto-Slavic, but, probably, *-ō in the Southern languages (Lat. quō “whither”, Gk. ἄνω “upwards”). This proposal finds support in the fossilized compound Lith. žmogus/žmogus 'man' ( OLat. -ōd, Gk. dial. ϝοικ-ω, but Bl.-Sl. *-ā(d) (Lith. -o, OCS -a).