Teräshallin alustava rakennesuunnittelu

Opinnäytetyön aiheena oli teräsrunkoisen konepajahallin alustava rakennesuunnittelu. Hallin leveys on 31 m, pituus 48,5 m ja vapaa korkeus 7 m. Kantava runko koostuu teräspilareista ja teräsprofiileista kootuista kattoristikoista. Työssä suunniteltiin rungon jäykistystapa, laskettiin kuormat ja mitoitettiin keskeiset ranneosat. Jäykistystapana vertailtiin levy- ja mastopilarijäykistystä. Keskeisenä tavoitteena oli tehdä tilaajalle konepajahallin rakennemalli. Aluksi halli mallinnettiin Tekla Structures -ohjelmalla, jossa luotiin rakennemallin analyysimalli. Se muutettiin FEMDesignissa avattavaan struxml-muotoon Tekla StruXML Export -työkalulla. Seuraavaksi laskettiin rakenteisiin kohdistuvat omapainosta, lumesta, tuulesta ja nosturista aiheutuvat kuormat. Tämän jälkeen laskettiin levyjäykistyksessä päätyseinien vinositeiden kautta perustukselle välittyvät voimat. Lopuksi rakenneosat mitoitettiin FEM-Design 17 3d Structure -ohjelmassa levy- ja mastopilarijäykistyksellä. Mitoituksen tulokseksi saadut profiilit päivitettiin malliin. Lopputuloksena valmistui konepajahallin 3d-malli. Hallin pituus johtaa siihen, että levyjäykistyksellä päätyseinien vinositeiden kautta perustukselle välittyy suhteellisen suuri voima. Tämä pyrkii nostamaan perustuksen ylös, jolloin omapainot eivät riitä pitämään perustusta paikoillaan. Johtopäätöksenä todettiin että, suositeltava jäykistystapa kyseiseen kohteeseen on mastopilarijäykistys. Raportista selviää myös, miten Tekla-malli viedään FEM-Design -ohjelmaan.The subject of the this final project was to design the steel structures of a machine workshop. The width of the hall is 31 m, the length 48,5 m and free height 7 m. The load-bearing frame consists of steel columns and trusses. The work included designing frame bracing, calculating loads and designing the main components. As bracing methods bracing plate and rigid column were compared. The main objective was to create a structural model of a hall for the client. At first, the hall was modeled using the Tekla Structures program, with which an analysis model of the structural model was created. It was converted to the struxml-format to be opened in FEM-Design using the Tekla StruXML Export tool. Next, the loads caused by snow, wind and the crane were calculated. Then the forces that come to the foundations through the diagonals of the end walls were calculated. Finally, the structural elements were dimensioned in FEM-Design 17 3d Structure with both bracing methods. The resulting profiles were updated in the structural model. As a result, a 3d-model of the hall was completed. The length of the hall leads to a relatively large force transmitted to the foundation by using the diagonals of the end walls. This tends to lift the foundation up, which means that the own weights are not enough to keep the foundation in place. As a conclusion, it was found that a more preferable bracing method for this subject would be rigid columns. The thesis also shows how the Tekla model is converted to FEM-Design

Abstract ARTICLE IN PRESS Computer Speech and Language xxx (2005) xxx–xxx Unlimited vocabulary speech recognition with morph language models applied to Finnish

In the speech recognition of highly inflecting or compounding languages, the traditional word-based language modeling is problematic. As the number of distinct word forms can grow very large, it becomes difficult to train language models that are both effective and cover the words of the language well. In the literature, several methods have been proposed for basing the language modeling on sub-word units instead of whole words. However, to our knowledge, considerable improvements in speech recognition performance have not been reported. In this article, we present a language-independent algorithm for discovering word fragments in an unsupervised manner from text. The algorithm uses the Minimum Description Length principle to find an inventory of word fragments that is compact but models the training text effectively. Language modeling and speech recognition experiments show that n-gram models built over these fragments perform better than n-gram models based on words. In two Finnish recognition tasks, relative error rate reductions between 12 % and 31 % are obtained. In addition, our experiments suggest that word fragments obtained using grammatical rules do not outperform the fragments discovered from text. We also present our recognition system and discuss how utilizing fragments instead of words affects the decoding process