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New aeronautical technologies like the Airframe Digital Twin, Virtual Fatigue Testing, and Probabilistic Damage Tolerance Analysis require a very large number of crack growth evaluations with a comprehensive number of random variables in order to accurately predict the fatigue life, the structural risk, or the remaining useful life of a structure. Current state-of-the-art crack growth methodologies and probabilistic methods do not make these new technologies possible due to limitations on computational speed, number of random variables, and statistical tools. In this work, a new computational strategy is developed and demonstrated such that several random variables directly affecting the crack growth analysis can be considered. This approach provides the opportunity for a more comprehensive and accurate digital twin evaluation, virtual testing prediction, and risk assessment, hence, improving aircraft design, safety, and reliability.

Preserving significant buildings not only saves the structure, but also the history of the United States for future generations. The availability of a fast and cost-effective monitoring system could help persuade more people to rehabilitate historic structures rather than building a new structure in its place. The main goals of this study are to develop a wireless sensor network (WSN) for the specific application of historic structures and to conduct a feasibility test in the field. The structure considered is a historic masonry church with a timber-framed roof. During the construction process, the foundations along the exterior walls are underpinned and the floors are removed and replaced with a floating concrete slab, topped with stone. On completion of the structural foundation work, the WSN is evident as an effective instrument for monitoring of historic structures.

Arisk assessment evaluation of the continued operational safety of the general aviationfleet canprovide important insight to the criticality/severity of a potentially serious structural issue. As such, the methodology and a computer code, SMART|LD, were developed to address risk assessment and risk management of general aviation structural issues. This information will provide a proactive approach to enable a nonbiased review of data to assure airworthiness. To address variability and uncertainties in loading and material properties, a probabilistic methodology was developed and implemented that considers the random loading and probabilistic stress-life material behavior developed from constant amplitude tests. The stress severity factor is used to account for the geometry effects (notches, fastener holes, etc.). Failure is determined using the Miner damage index with the index calibrated from simulations of variable amplitude tests. Monte Carlo sampling is used to calculate the structural probability of failure, or mean and standard deviation of flights and hours to failure, the hazard function, and sensitivity analysis. A numerical example is presented to demonstrate the methodology.

The preservation of the history of the United States through its significant buildings is critical; however, this initiative is currently threatened due to the modernization of the nation’s infrastructure. If a fast and cost-effective way to monitor the condition of a historic structure existed, many more structures could be rehabilitated for modern uses while preserving the important historic content. Widely accessible wireless sensor network (WSN) technology could be a great asset to the preservation of historic structures in the future. The main objectives of this work are to develop a reliable WSN that is tailored for use in historic structures, and to implement the system in a structure undergoing rehabilitation. The structure considered is an historic wooden church in which the foundation requires replacement. Sensors will monitor tilt of the church’s walls throughout construction. During the construction process, the entire floor of the church is removed and the tree stump foundations are replaced by concrete masonry unit (CMU) blocks and steel pedestals. The tilt in the walls is correlated to the construction process. Through this research, it can be seen that the WSN is an effective tool for structural monitoring in historic preservation.

Web page cleaning is one of the most essential tasks in Web corpus construction. The intention is to separate the main content from navigational elements, templates, and advertisements, often referred to as boilerplate. In this paper, we particularly enhance Web page cleaning applied to pages containing comments and introduce a new training corpus for that purpose. Beside extending an existing boilerplate detection algorithm by means of a comment classifier, we train and test different classifiers on extended feature sets solving a two-class problem (content vs. boilerplate) on our and an existing benchmark corpus. Results show that the proposed approach outperforms existing methods, particularly on comment pages from different domains. Finally, we point out that our trained classifiers are domain independent and with small adjustments only transferable to other languages.

Bridges are bottlenecks in the railroad net, because of their limiting characteristics. To achieve a high load of the railroads old bridges especially are being pushed to their physical limit, regarding transfer speed, schedule, axle load and train length. Therefore monitoring of these strategic structures is getting more and more important. Installation costs of conventional sensors are expensive and time intensive. New wireless sensor platforms and distributed processing algorithms, going hand in hand with new or enhanced monitoring methods, promise an early damage detection and damage estimation. This paper designs a wireless sensor network for Structural Health Monitoring using commercially available wireless sensors to measure and extract vibration characteristics of bridges. The functionality of the network is verified in a laboratory experiment.

For planning, analysis, and optimization of modern, self organizing radio networks field strength prediction plays an essential role. A huge number of predictions is needed for planning purposes while high quality and level of detail are important as well. In this paper, we present a flexible ray optical approach for radio wave propagation to meet those demands. This approach allows easy exchange of the path loss model and produces multi path information enabling for environment aware MIMO channel models.

Fast and accurate path loss prediction is a prerequisite for effective planning and optimization of cellular radio networks [1]. Ray optical algorithms often achieve very high prediction accuracy, see [2], but consume much computation time. However, semi-empirical prediction models suffer from inherent low prediction quality but require reasonable computational effort.

*† ‡ § ** †† ‡‡ §§ This paper describes the development of a probabilistic methodology that can perform risk assessment of small airplanes. The objective was to develop a comprehensive probabilistic methodology to allow Federal Aviation Administration (FAA) engineers to conduct a risk assessment of general aviation (GA) structural issues in support of policy decisions. Requisite-supporting technology and data issues, in particular, probability distributions of relevant inputs, were investigated and developed so that a realistic risk assessment could be obtained. Example problems are presented to demonstrate the methodology that includes the calculation of flights (or hours)-to-failure and the probabilityof-failure for a specified number of flying hours. Representative sensitivity studies were also conducted to determine significant variables.

Acceleration techniques on graphics hardware for field strength prediction algorithms are presented. We identify some basic building blocks of common propagation algorithms and propose methods which can be very efficiently implemented on graphics processing units. We show that the use of such acceleration techniques leads to huge speedups in the evaluation time of propagation algorithms.