Non linear response of planar asymmetric systems

The seismic behaviour of one-storey asymmetric structures has been studied since 1970s by a number of researches studies which identified the coupled nature of the translational-to-torsional response of those class of systems leading to severe displacement magnifications at the perimeter frames and therefore to significant increase of local peak seismic demand to the structural elements with respect to those of equivalent not-eccentric systems (Kan and Chopra 1987). These studies identified the fundamental parameters (such as the fundamental period TL normalized eccentricity e and the torsional-to-lateral frequency ratio Ωϑ) governing the torsional behavior of in-plan asymmetric structures and trends of behavior. It has been clearly recognized that asymmetric structures characterized by Ωϑ >1, referred to as torsionally-stiff systems, behave quite different form structures with Ωϑ <1, referred to as torsionally-flexible systems. Previous research works by some of the authors proposed a simple closed-form estimation of the maximum torsional response of one-storey elastic systems (Trombetti et al. 2005 and Palermo et al. 2010) leading to the so called “Alpha-method” for the evaluation of the displacement magnification factors at the corner sides. The present paper provides an upgrade of the “Alpha Method” removing the assumption of linear elastic response of the system. The main objective is to evaluate how the excursion of the structural elements in the inelastic field (due to the reaching of yield strength) affects the displacement demand of one-storey in-plan asymmetric structures. The system proposed by Chopra and Goel in 2007, which is claimed to be able to capture the main features of the non-linear response of in-plan asymmetric system, is used to perform a large parametric analysis varying all the fundamental parameters of the system, including the inelastic demand by varying the force reduction factor from 2 to 5. Magnification factors for different force reduction factor are proposed and comparisons with the results obtained from linear analysis are provided.

The influence of infill walls on seismic behavior of a historical building in Shanghai

Seismic assessment and seismic strengthening are the key issues need to be figured out during the process of protection and reusing of historical buildings. In this thesis the seismic behaviors of the hinged steel structure, a typical structure of historical buildings, i.e. hinged steel frames in Shanghai, China, were studied based on experimental investigations and theoretic analysis. How the non-structural members worked with the steel frames was analyzed thoroughly. Firstly, two 1/4 scale hinged steel frames were constructed based on the structural system of Bund 18, a historical building in Shanghai: M1 model without infill walls, M2 model with infill walls, and tested under the horizontal cyclic loads to investigate their seismic behavior. The Shaking Table Test and its results indicated that the seismic behavior of the hinged steel frames could be improved significantly with the help of non-structural members, i.e., surrounding elements outside the hinged steel frames and infilled walls. To specify, the columns are covered with bricks, they consist of I shape formed steel sections and steel plates, which are clenched together. The steel beams are connected to the steel column by steel angle, thus the structure should be considered as a hinged frame. And the infilled wall acted as a compression diagonal strut to withstand the horizontal load, therefore, the seismic capacity and stiffness of the hinged steel frames with infilled walls could be estimated by using the equivalent compression diagonal strut model. A SAP model has been constructed with the objective to perform a dynamic nonlinear analysis. The obtained results were compared with the results obtained from Shaking Table Test. The Test Results have validated that the influence of infill walls on seismic behavior can be estimated by using the equivalent diagonal strut model.

Frame-driven Extraction of Linked Data and Ontologies from Text

Ontology design and population -core aspects of semantic technologies- re- cently have become fields of great interest due to the increasing need of domain-specific knowledge bases that can boost the use of Semantic Web. For building such knowledge resources, the state of the art tools for ontology design require a lot of human work. Producing meaningful schemas and populating them with domain-specific data is in fact a very difficult and time-consuming task. Even more if the task consists in modelling knowledge at a web scale. The primary aim of this work is to investigate a novel and flexible method- ology for automatically learning ontology from textual data, lightening the human workload required for conceptualizing domain-specific knowledge and populating an extracted schema with real data, speeding up the whole ontology production process. Here computational linguistics plays a fundamental role, from automati- cally identifying facts from natural language and extracting frame of relations among recognized entities, to producing linked data with which extending existing knowledge bases or creating new ones. In the state of the art, automatic ontology learning systems are mainly based on plain-pipelined linguistics classifiers performing tasks such as Named Entity recognition, Entity resolution, Taxonomy and Relation extraction [11]. These approaches present some weaknesses, specially in capturing struc- tures through which the meaning of complex concepts is expressed [24]. Humans, in fact, tend to organize knowledge in well-defined patterns, which include participant entities and meaningful relations linking entities with each other. In literature, these structures have been called Semantic Frames by Fill- 6 Introduction more [20], or more recently as Knowledge Patterns [23]. Some NLP studies has recently shown the possibility of performing more accurate deep parsing with the ability of logically understanding the structure of discourse [7]. In this work, some of these technologies have been investigated and em- ployed to produce accurate ontology schemas. The long-term goal is to collect large amounts of semantically structured information from the web of crowds, through an automated process, in order to identify and investigate the cognitive patterns used by human to organize their knowledge.