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Keywords
- Data, information and knowledge modeling in civil engineering; Function theoretic methods and PDE in engineering sciences; Mathematical methods for (robotics and) computer vision; Numerical modeling in engineering; Optimization in engineering applications (21) (remove)
In construction engineering, a schedule’s input data, which is usually not exactly known in the planning phase, is considered deterministic when generating the schedule. As a result, construction schedules become unreliable and deadlines are often not met. While the optimization of construction schedules with respect to costs and makespan has been a matter of research in the past decades, the optimization of the robustness of construction schedules has received little attention. In this paper, the effects of uncertainties inherent to the input data of construction schedules are discussed. Possibilities are investigated to improve the reliability of construction schedules by considering alternative processes for certain tasks and by identifying the combination of processes generating the most robust schedule with respect to the makespan of a construction project.
In photogrammetry and computer vision the trifocal tensor is used to describe the geometric relation between projections of points in three views. In this paper we analyze the stability and accuracy of the metric trifocal tensor for calibrated cameras. Since a minimal parameterization of the metric trifocal tensor is challenging, the additional constraints of the interior orientation are applied to the well-known projective 6-point and 7-point algorithms for three images. The experimental results show that the linear 7-point algorithm fails for some noise-free degenerated cases, whereas the minimal 6-point algorithm seems to be competitive even with realistic noise.
A topology optimization method has been developed for structures subjected to multiple load cases (Example of a bridge pier subjected to wind loads, traffic, superstructure...). We formulate the problem as a multi-criterial optimization problem, where the compliance is computed for each load case. Then, the Epsilon constraint method (method proposed by Chankong and Haimes, 1971) is adapted. The strategy of this method is based on the concept of minimizing the maximum compliance resulting from the critical load case while the other remaining compliances are considered in the constraints. In each iteration, the compliances of all load cases are computed and only the maximum one is minimized. The topology optimization process is switching from one load to another according to the variation of the resulting compliance. In this work we will motivate and explain the proposed methodology and provide some numerical examples.
In this paper we present some rudiments of a generalized Wiman-Valiron theory in the context of polymonogenic functions. In particular, we analyze the relations between different notions of growth orders and the Taylor coefficients. Our main intention is to look for generalizations of the Lindel¨of-Pringsheim theorem. In contrast to the classical holomorphic and the monogenic setting we only obtain inequality relations in the polymonogenic setting. This is due to the fact that the Almansi-Fischer decomposition of a polymonogenic function consists of different monogenic component functions where each of them can have a totally different kind of asymptotic growth behavior.
Sensor faults can affect the dependability and the accuracy of structural health monitoring (SHM) systems. Recent studies demonstrate that artificial neural networks can be used to detect sensor faults. In this paper, decentralized artificial neural networks (ANNs) are applied for autonomous sensor fault detection. On each sensor node of a wireless SHM system, an ANN is implemented to measure and to process structural response data. Structural response data is predicted by each sensor node based on correlations between adjacent sensor nodes and on redundancies inherent in the SHM system. Evaluating the deviations (or residuals) between measured and predicted data, sensor faults are autonomously detected by the wireless sensor nodes in a fully decentralized manner. A prototype SHM system implemented in this study, which is capable of decentralized autonomous sensor fault detection, is validated in laboratory experiments through simulated sensor faults. Several topologies and modes of operation of the embedded ANNs are investigated with respect to the dependability and the accuracy of the fault detection approach. In conclusion, the prototype SHM system is able to accurately detect sensor faults, demonstrating that neural networks, processing decentralized structural response data, facilitate autonomous fault detection, thus increasing the dependability and the accuracy of structural health monitoring systems.
Modern distributed engineering applications are based on complex systems consisting of various subsystems that are connected through the Internet. Communication and collaboration within an entire system requires reliable and efficient data exchange between the subsystems. Middleware developed within the web evolution during the past years provides reliable and efficient data exchange for web applications, which can be adopted for solving the data exchange problems in distributed engineering applications. This paper presents a generic approach for reliable and efficient data exchange between engineering devices using existing middleware known from web applications. Different existing middleware is examined with respect to the suitability in engineering applications. In this paper, a suitable middleware is shown and a prototype implementation simulating distributed wind farm control is presented and validated using several performance measurements.
IFC-BASED MONITORING INFORMATION MODELING FOR DATA MANAGEMENT IN STRUCTURAL HEALTH MONITORING
(2015)
This conceptual paper discusses opportunities and challenges towards the digital representation of structural health monitoring systems using the Industry Foundation Classes (IFC) standard. State-of-the-art sensor nodes, collecting structural and environmental data from civil infrastructure systems, are capable of processing and analyzing the data sets directly on-board the nodes. Structural health monitoring (SHM) based on sensor nodes that possess so called “on-chip intelligence” is, in this study, referred to as “intelligent SHM”, and the infrastructure system being equipped with an intelligent SHM system is referred to as “intelligent infrastructure”. Although intelligent SHM will continue to grow, it is not possible, on a well-defined formalism, to digitally represent information about sensors, about the overall SHM system, and about the monitoring strategies being implemented (“monitoring-related information”). Based on a review of available SHM regulations and guidelines as well as existing sensor models and sensor modeling languages, this conceptual paper investigates how to digitally represent monitoring-related information in a semantic model. With the Industry Foundation Classes, there exists an open standard for the digital representation of building information; however, it is not possible to represent monitoring-related information using the IFC object model. This paper proposes a conceptual approach for extending the current IFC object model in order to include monitoring-related information. Taking civil infrastructure systems as an illustrative example, it becomes possible to adequately represent, process, and exchange monitoring-related information throughout the whole life cycle of civil infrastructure systems, which is referred to as monitoring information modeling (MIM). However, since this paper is conceptual, additional research efforts are required to further investigate, implement, and validate the proposed concepts and methods.
In this paper, we present an empirical approach for objective and quantitative benchmarking of optimization algorithms with respect to characteristics induced by the forward calculation. Due to the professional background of the authors, this benchmarking strategy is illustrated on a selection of search methods in regard to expected characteristics of geotechnical parameter back calculation problems. Starting from brief introduction into the approach employed, a strategy for optimization algorithm benchmarking is introduced. The benchmarking utilizes statistical tests carried out on well-known test functions superposed with perturbations, both chosen to mimic objective function topologies found for geotechnical objective function topologies. Here, the moved axis parallel hyper-ellipsoid test function and the generalized Ackley test function in conjunction with an adjustable quantity of objective function topology roughness and fraction of failing forward calculations is analyzed. In total, results for 5 optimization algorithms are presented, compared and discussed.
SELECTION AND SCALING OF GROUND MOTION RECORDS FOR SEISMIC ANALYSIS USING AN OPTIMIZATION ALGORITHM
(2015)
The nonlinear time history analysis and seismic performance based methods require a set of scaled ground motions. The conventional procedure of ground motion selection is based on matching the motion properties, e.g. magnitude, amplitude, fault distance, and fault mechanism. The seismic target spectrum is only used in the scaling process following the random selection process. Therefore, the aim of the paper is to present a procedure to select a sets of ground motions from a built database of ground motions. The selection procedure is based on running an optimization problem using Dijkstra’s algorithm to match the selected set of ground motions to a target response spectrum. The selection and scaling procedure of optimized sets of ground motions is presented by examining the analyses of nonlinear single degree of freedom systems.