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1. Large-scale urban building function mapping by integrating multi-source web-based geospatial data

   https://doi.org/10.1080/10095020.2023.2264342  

   Chen, Wei ; Zhou, Yuyu ; Stokes, Eleanor C. ; Zhang, Xuesong ( October 2023 , Geo-spatial Information Science)

   Morphological (e.g. shape, size, and height) and function (e.g. working, living, and shopping) information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling. Due to the limited availability of socio-economic geospatial data, it is more challenging to map building functions than building morphological information, especially over large areas. In this study, we proposed an integrated framework to map building functions in 50 U.S. cities by integrating multi-source web-based geospatial data. First, a web crawler was developed to extract Points of Interest (POIs) from Tripadvisor.com, and a map crawler was developed to extract POIs and land use parcels from Google Maps. Second, an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints. Third, the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions (i.e. hospital, hotel, school, shop, restaurant, and office). The accuracy assessment indicates that the proposed framework performed well, with an average overall accuracy of 94% and a kappa coefficient of 0.63. With the worldwide coverage of Google Maps and Tripadvisor.com, the proposed framework is transferable to other cities over the world. The data products generated from this study are of great use for quantitative city-scale urban studies, such as building energy use modeling at the single building level over large areas. 

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2. Design and Assessment of Innovative Dual-Mode Rolling Isolation Systems

   https://doi.org/11244/321054  

   Hadikhan Tehrani, Mohammad ( August 2019 , The University of Oklahoma Libraries)

   null (Ed.)

   Loss of operation or devastating damage to buildings and industrial structures, as well as equipment housed in them, has been observed due to earthquake-induced vibrations. A common source of operational downtime is due to the performance reduction of vital equipment, which are sensitive to the total transmitted acceleration. A well-known method of protecting such equipment is seismic isolation of the equipment itself (or a group of equipment), as opposed to the entire structure due to the lower cost of implementation. The first objective of this dissertation is assessing a rolling isolation system (RIS) based on existing design guidelines for telecommunications equipment. A discrepancy is observed between the required response spectrum (RRS) and the one and only accelerogram recommended in the guideline. Several filters are developed to generate synthetic accelerograms that are compatible with the RRS. The generated accelerograms are used for probabilistic assessment of a RIS that is acceptable per the guideline. This assessment reveals large failure probability due to displacement demands in excess of the displacement capacity of the RIS. When the displacement demands on an isolation system are in excess of its capacity, impacts result in spikes in transmitted acceleration. Therefore, the second objective of this dissertation is to design impact prevention/mitigation mechanisms. A dual-mode system is proposed where the behavior changes when the displacement exceeds a predefined threshold. A new piecewise optimal control approach is developed and applied to find the best possible mechanism for the region beyond the threshold. By utilizing the designed curves obtained from the proposed optimal control procedure, a Kelvin-Voigt device is tuned for illustrative purposes. On the other hand, the preference for protecting equipment decreases as the earthquake intensity increases. In extreme seismic loading, the response mitigation of the primary structure (i.e., life safety and collapse prevention) is of greater concern than protecting isolated equipment. Therefore, the third objective of this dissertation is to develop an innovative dual-mode system that can behave as equipment isolation under low to moderate seismic loading and passively transition to behave as a vibration absorber for the primary structure under extreme seismic loading. To reduce the computational cost of simulating a large linear elastic structure with nonlinear attachments (i.e., equipment isolation with cubic hardening nonlinearity), a reduced order modeling method is introduced that can capture the behavior of such nonlinear coupled systems. The method is applied to study the feasibility of dual-mode vibration isolation/absorber. To this end, nonlinear transmissibility curves for the roof displacement and isolated mass total acceleration are developed from the steady-state responses of dual-mode systems using the harmonic balanced method. The final objective of this dissertation is to extend the reduced order modeling method developed for linear elastic structure with nonlinear attachment to inelastic structures (without attachments). The new inelastic model condensation (IMC) method uses the modal properties of the full structural model (in the elastic range) to construct a linear reduced order model in conjunction with a hysteresis model to capture the hysteretic inter-story restoring forces. The parameters of these hysteretic forces are easily tuned, in order to fit the inelastic behavior of the condensed structure to that of the full model under a variety of simple loading scenarios. The fidelity of structural models condensed in this way is demonstrated via simulation for different ground motion intensities on three different building structures with various heights. The simplicity, accuracy, and efficiency of this approach could significantly alleviate the computational burden of performance-based earthquake engineering. 

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3. A MACHINE LEARNING APPROACH FOR IMPACT DAMAGE QUANTIFICATION IN POLYMER MATRIX COMPOSITES

   https://doi.org/10.12783/asc37/36412  

   BERKOWITZ, KATHERINE ; GUHA, RISHABH D. ; OLUWAJIRE, OLUWATIMILEHIN ; GRACE, LANDON ( September 2022 , American Society for Composites)

   Largely due to superior properties compared to traditional materials, the use of polymer matrix composites (PMC) has been expanding in several industries such as aerospace, transportation, defense, and marine. However, the anisotropy and nonhomogeneity of these structures contribute to the difficulty in evaluating structural integrity; damage sites can occur at multiple locations and length scales and are hard to track over time. This can lead to unpredictable and expensive failure of a safety-critical structure, thus creating a need for non-destructive evaluation (NDE) techniques which can detect and quantify small-scale damage sites and track their progression. Our research group has improved upon classical microwave techniques to address these needs; utilizing a custom device to move a sample within a resonant cavity and create a spatial map of relative permittivity. We capitalize on the inevitable presence of moisture within the polymer network to detect damage. The differing migration inclinations of absorbed water molecules in a pristine versus a damaged composite alters the respective concentrations of the two chemical states of moisture. The greater concentration of free water molecules residing in the damage sites exhibit highly different relative permittivity when compared to the higher ratio of polymer-bound water molecules in the undamaged areas. Currently, the technique has shown the ability to detect impact damage across a range of damage levels and gravimetric moisture contents but is not able to specifically quantify damage extent with regards to impact energy level. The applicability of machine learning (ML) to composite materials is substantial, with uses in areas like manufacturing and design, prediction of structural properties, and damage detection. Using traditional NDE techniques in conjunction with supervised or unsupervised ML has been shown to improve the accuracy, reliability, or efficiency of the existing methods. In this work, we explore the use of a combined unsupervised/supervised ML approach to determine a damage boundary and quantification of single-impact specimens. Dry composite specimens were damaged via drop tower to induce one central impact site of 0, 2, or 3 Joules. After moisture exposure, Entrepreneur Dr, Raleigh, North Carolina 27695, U.S.A. 553 each specimen underwent dielectric mapping, and spatial permittivity maps were created at a variety of gravimetric moisture contents. An unsupervised K-means clustering algorithm was applied to the dielectric data to segment the levels of damage and define a damage boundary. Subsequently, supervised learning was used to quantify damage using features including but not limited to thickness, moisture content, permittivity values of each cluster, and average distance between points in each cluster. A regression model was trained on several samples with impact energy as the predicted variable. Evaluation was then performed based on prediction accuracy for samples in which the impact energies are not known to the model.

    

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4. Robust component: a robustness measure that incorporates access to critical facilities under disruptions

   https://doi.org/10.1098/rsif.2019.0149  

   Dong, Shangjia ; Wang, Haizhong ; Mostafavi, Ali ; Gao, Jianxi ( August 2019 , Journal of The Royal Society Interface)

   The objective of this paper is to integrate the post-disaster network access to critical facilities into the network robustness assessment, considering the geographical exposure of infrastructure to natural hazards. Conventional percolation modelling that uses generating function to measure network robustness fails to characterize spatial networks due to the degree correlation. In addition, the giant component alone is not sufficient to represent the performance of transportation networks in the post-disaster setting, especially in terms of the access to critical facilities (i.e. emergency services). Furthermore, the failure probability of various links in the face of different hazards needs to be encapsulated in simulation. To bridge this gap, this paper proposed the metric robust component and a probabilistic link-removal strategy to assess network robustness through a percolation-based simulation framework. A case study has been conducted on the Portland Metro road network during an M9.0 earthquake scenario. The results revealed how the number of critical facilities severely impacts network robustness. Besides, earthquake-induced failures led to a two-phase percolation transition in robustness performance. The proposed robust component metric and simulation scheme can be generalized into a wide range of scenarios, thus enabling engineers to pinpoint the impact of disastrous disruption on network robustness. This research can also be generalized to identify critical facilities and sites for future development. 

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5. A Utility-Based Path Planning for Safe UAS Operations with a Task-Level Decision-Making Capability

   https://doi.org/10.1109/SMC.2019.8914226  

   Kaya, Uluhan C. ; Dogan, Atilla ; Huber, Manfred ( October 2019 , 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC))

   Unmanned aircraft systems (UAS) are being used more and more every day in almost any area to solve challenging real-life problems. Increased autonomy and advancements in low-cost high-computing technologies made these compact autonomous solutions accessible to any party with ease. However, this ease of use brings its own challenges that need to be addressed. In an autonomous flight scenario over a public space, an autonomous operation plan has to consider the public safety and regulations as well as the task specific objectives. In this work, we propose a generic utility function for the path planning of UAS operations that includes the benefits of accomplishing the goals as well as the safety risks incurred along the flight trajectories, with the purpose of making task-level decisions through the optimization of the carefully constructed utility function for a given scenario. As an optimizer, we benefited from a multi-tree variant of the optimal T-RRT * (Multi-T-RRT * path planning algorithm. To illustrate its operation, results of simulation of a UAS scenario are presented. 

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