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Parallel computing in civil engineering has been restricted to monotonic shock or blast loading with explicit algorithm which is characteristically feasible to be parallelized. In the present paper, efficient parallelization strategies for the highly demanded implicit nonlinear finite-element analysis (FEA) program for real scale reinforced concrete (RC) structures under cyclic loading are proposed. Quantitative comparison of state-of-the-art parallel strategies in terms of factorization were carried out, leading to the problem-optimized solver, which successfully embraces the penalty method and banded nature. Particularly, the penalty method employed imparts considerable smoothness to the global response, which yields practical superiority of the parallel triangular system solution over those of advanced solvers such as the parallel preconditioned conjugate gradient method. Other salient issues on parallelization are also addressed. By virtue of the parallelization, the analysis platform offers unprecedented access to physics-based mechanisms and probabilistic randomness at theentire system level and realistically reproduces global degradation and localized damage, as reflected from the application to a RC structure. Equipped with accuracy, stability and scalability, the parallel platform is believed to serve as a fertile ground for the introducing of further physical mechanisms into various research fields, as well as the earthquake engineering community. [ABSTRACT FROM AUTHOR]

Seismic early warning has been very important and has become feasible in Taiwan. Perhaps because of the lack of quick and reliable estimations of the induced structural response, however, the triggering criteria of almost all of the existing earthquake protection or early warning systems in the world are merely based on the collected or estimated data of the ground motion, without any information regarding the structural response. This paper presents a methodology of generating quick seismic response estimations of a prestressed concrete (PC) bridge using artificial neural networks (ANNs), which may be incorporated in a seismic early warning system for the bridge. In the methodology ANNs were applied to model the critical structural response of a PC bridge subjected to earthquake excitation of various magnitudes along various directions. The objective was to implement a well-trained network that is capable of providing a quick prediction for the critical response of the target bridge. The well-known multilayer perception (MLP) networks with back propagation algorithm were employed. A simple augmented form of MLP that can be quantitatively determined was proposed. These networks were trained and tested based on the analytical data obtained from the nonlinear dynamic finite fiber element analyses of the target PC bridge. The augmented MLPs were found to be much more efficient than the MLPs in modeling the critical bending moments of the piers and girder of the PC bridge. [ABSTRACT FROM AUTHOR]

Distributed hybrid simulation is an approach to large-scale testing in which the system under test is split into several sub-structures which are tested or simulated in different locations. Data are passed between the sub-structures at each timestep so as to ensure that the distributed experiment realistically simulates the full system under test. This approach optimises the use of resources at different locations to achieve a more representative experiment. While different software to conduct distributed simulations exists, there are no standards and specifications to organise and plan the experiments, and as a result the different systems lack inter-operability. To address these issues, we have developed a high-level specification called Celestina, which provides a framework for conducting a distributed experiment. Celestina specifies the services to be implemented, under three main headings of networking, definition and execution, and supports the data exchange during a simulation. It does not force any particular implementation or method of data exchange. This paper summarises the Celestina specification and describes one implementation. Lastly, a validation experiment is presented, involving distributed numerical simulations of an earlier local hybrid experiment, in which Celestina controls the experiment planning and data exchange effectively and with minimal computational overhead. [ABSTRACT FROM AUTHOR]

Traditional sensors, such as accelerometers and displacement transducers, are widely used in laboratory and field experiments in earthquake engineering to measure the motions of both structural and nonstructural components. Such sensors, however, must be physically attached to the structure and require cumbersome cabling and configurations and substantial time for setup. For reduced-scale experiments, these conventional sensors may substantially alter the dynamic properties of the system by changing the mass, stiffness, and damping properties of the specimen. Moreover, it is very difficult with traditional sensors to capture the three-dimensional motions of light or oddly shaped components such as microscopes, computers, or other building contents. In this paper, the methodology of light-based motion tracking is applied to the measurement of the three-dimensional motions of various types of equipment and building contents commonly found in biological and chemical science laboratories. The system is comprised of six high-speed, high-resolution charge-coupled-device (CCD) cameras outfitted with a cluster of red-light emitting diodes (LEDs). Retroreflective (passive) spherical markers discretely located in a scene are tracked in time and used to describe the behavior of various types of equipment and contents subjected to a range of earthquake motions. Results from this study show that the nonintrusive, light-based approach is extremely promising in terms of its ability to capture relative displacements in three orthogonal directions and complementary rotations. [ABSTRACT FROM AUTHOR]

In common with many scientific disciplines, earthquake engineering research is increasingly focusing on large international collaborations to address complex problems. Data integration is a key requirement to facilitate joint research efforts and improve experimental outcomes. Development and implementation of a novel virtual database is presented, linking 22 leading European earthquake engineering research institutions, making data integration possible at a European level without the need for a large, centralized repository. The importance of establishing appropriate work methodologies to succeed in a distributed and highly heterogeneous work environment involving many dispersed institutions is described. The future of earthquake engineering data integration is considered, highlighting Semantic Web technologies as the main technological foundation to lead this integration. [ABSTRACT FROM AUTHOR]

Machine translation is able to support cross-language information retrieval (CLIR) applications given the availability of high-quality translation resources. This study explores the effectiveness of machine translation for the domain of earthquake engineering within the bounds of Chinese-to-English CLIR applications. The writers first defined and then applied a set of procedures to generate Chinese-to-English translation resources for the selected domain. The generated resources are unique and different from general dictionaries or corpora because they address the Chinese segmentation process and are especially oriented for information retrieval (IR) applications. To test the quality of the resources, the writers converted a test collection of documents from Chinese into English through machine translation and conducted trial retrieval runs on the translated documents using different retrieval models. For comparison purposes, the same retrieval runs were also tested on the expert-written English translations. The results suggest that IR-based machine translation was able to yield results comparable to that of human translation and the generated resources had the potential to enable greater sharing of earthquake engineering information across languages while reducing manual translation efforts. [ABSTRACT FROM AUTHOR]

Engineering analysis to quantify the effects of earthquake forces on the structural strength of components requires determining the damage mode and severity of the components. The analysis requires strength computations and visual damage assessments, which are information intensive, potentially error-prone, and slow. This study develops a building-information-modeling (BIM) based approach to support the engineering analysis of reinforced concrete structures. In the proposed approach, the damage information is represented along with the geometric, topological, and structural information. Transformation and reasoning mechanisms are proposed to utilize the information contained in the BIM to perform strength analysis and visual assessment tasks. The approach is validated on a case study building, which contains 42 damaged piers and spandrels. [ABSTRACT FROM AUTHOR]

The geotechnical earthquake engineering community often adopts empirically derived models. Unfortunately, the community has not embraced the value of model validation, leaving practitioners with little information on the uncertainties present in a given model and the model's predictive capability. In this study, we present a machine learning technique known as support vector regression (SVR) together with rigorous validation for modeling lateral spread displacements and outline how this information can be used for identifying gaps in the data set. We demonstrate the approach using the free face lateral displacement data. The results illustrate that the SVR has relatively better predictive capability than the commonly used empirical relationship derived using multilinear regression. Moreover, the analysis of the SVR model and its support vectors helps in identifying gaps in the data and defining the scope for future data collection. [ABSTRACT FROM AUTHOR]