More Like this

1. The development of Gaussian process regression for effective regional post‐earthquake building damage inference

   https://doi.org/10.1111/mice.12630  

   Sheibani, Mohamadreza; Ou, Ge (October 2020, Computer-Aided Civil and Infrastructure Engineering)

   Abstract Post‐earthquake reconnaissance survey of structural damage is an effective way of documenting and understanding the impact of earthquakes on structures. This article aims at providing an efficient data‐based framework that reduces the required time for reconnaissance missions and predicts the damage intensities for every building in the affected region. We hypothesize that a joint selection of necessary structural and earthquake parameters along with sparse damage observations are sufficient to train a supervised learning algorithm and accurately infer the damage for other buildings in the region. Gaussian process regression is employed to prove the hypothesis for probabilistic inference of different damage indices. The algorithm performs efficiently by selecting a set of diverse and representative buildings for damage observations using K‐medoids clustering. To validate the hypothesis and the proposed method, the algorithm framework is implemented on two severe earthquake simulation testbeds. The impacts of different building and ground motion variables on the damage inference performance are discussed. Furthermore, the effectiveness of observation sampling by clustering in the post‐earthquake damage inference is compared with random sampling. 

   more » « less
2. In-event surrogate model training for regional transmission tower-line system dynamic responses prediction in realistic hurricane wind fields

   https://doi.org/10.1080/15732479.2024.2345853  

   Zhu, Xiao; Ou, Ge; Pu, Zhaoxia; Li, Xin; Xue, Jiayue (April 2024, Structure and Infrastructure Engineering)

   Surrogate models have shown improved accuracy in predicting infrastructure responses during dynamic loadings. However, training a surrogate model for complex loading inputs across the entire hazard region remains challenging. This study provides insight into the training of surrogate models to estimate the responses of transmission tower-line structures in a coupled high-dimensional and high-resolution wind field and presents innovative methods for addressing these challenges. Four data- and physics-based spatial-temporal decoupling sampling methods are employed and cross-compared to obtain the most representative in-event wind profiles for training the surrogate model. Long Short-Term Memory (LSTM) is utilised as the surrogate model framework to predict the dynamic responses of the structure during the 2017 Hurricane Harvey. The accuracy and robustness of two transmission tower-line structure configuration surrogate models are validated by comparing the predictions with finite element analyses by using randomly distributed temporal and geospatial wind profiles throughout the hurricane. Finally, a single LSTM surrogate model is developed, trained by applying the full reference wind speed range of Hurricane Harvey for the regional-scale structural performance evaluation of the transmission tower-line system. The results demonstrate that the proposed surrogate model training methodology is general and can be applied to regional-scale structural performance evaluations. 

   more » « less
3. Surrogate modeling of structural seismic response using probabilistic learning on manifolds

   https://doi.org/10.1002/eqe.3839  

   Zhong, Kuanshi; Navarro, Javier G.; Govindjee, Sanjay; Deierlein, Gregory G. (February 2023, Earthquake Engineering & Structural Dynamics)

   Abstract Nonlinear response history analysis (NLRHA) is generally considered to be a reliable and robust method to assess the seismic performance of buildings under strong ground motions. While NLRHA is fairly straightforward to evaluate individual structures for a select set of ground motions at a specific building site, it becomes less practical for performing large numbers of analyses to evaluate either (1) multiple models of alternative design realizations with a site‐specific set of ground motions, or (2) individual archetype building models at multiple sites with multiple sets of ground motions. In this regard, surrogate models offer an alternative to running repeated NLRHAs for variable design realizations or ground motions. In this paper, a recently developed surrogate modeling technique, called probabilistic learning on manifolds (PLoM), is presented to estimate structural seismic response. Essentially, the PLoM method provides an efficient stochastic model to develop mappings between random variables, which can then be used to efficiently estimate the structural responses for systems with variations in design/modeling parameters or ground motion characteristics. The PLoM algorithm is introduced and then used in two case studies of 12‐story buildings for estimating probability distributions of structural responses. The first example focuses on the mapping between variable design parameters of a multidegree‐of‐freedom analysis model and its peak story drift and acceleration responses. The second example applies the PLoM technique to estimate structural responses for variations in site‐specific ground motion characteristics. In both examples, training data sets are generated for orthogonal input parameter grids, and test data sets are developed for input parameters with prescribed statistical distributions. Validation studies are performed to examine the accuracy and efficiency of the PLoM models. Overall, both examples show good agreement between the PLoM model estimates and verification data sets. Moreover, in contrast to other common surrogate modeling techniques, the PLoM model is able to preserve correlation structure between peak responses. Parametric studies are conducted to understand the influence of different PLoM tuning parameters on its prediction accuracy. 

   more » « less
4. An Intelligently Designed AI for Structural Health Monitoring of a Reinforced Concrete Bridge

   Locke, William R; Mokalled, Stefani C; Abuodeh, Omar R; Redmond, Laura M; McMahan, Christopher S (January 2021, The Concrete Industry in the Era of AI)

   e. With recent advances in online sensing technology and high-performance computing, structural health monitoring (SHM) has begun to emerge as an automated approach to the real-time conditional monitoring of civil infrastructure. Ideal SHM strategies detect and characterize damage by leveraging measured response data to update physics-based finite element models (FEMs). When monitoring composite structures, such as reinforced concrete (RC) bridges, the reliability of FEM based SHM is adversely affected by material, boundary, geometric, and other model uncertainties. Civil engineering researchers have adapted popular artificial intelligence (AI) techniques to overcome these limitations, as AI has an innate ability to solve complex and ill-defined problems by leveraging advanced machine learning techniques to rapidly analyze experimental data. In this vein, this study employs a novel Bayesian estimation technique to update a coupled vehicle-bridge FEM for the purposes of SHM. Unlike existing AI based techniques, the proposed approach makes intelligent use of an embedded FEM model, thus reducing the parameter space while simultaneously guiding the Bayesian model via physics-based principles. To validate the method, bridge response data is generated from the vehicle-bridge FEM given a set of “true” parameters and the bias and standard deviation of the parameter estimates are analyzed. Additionally, the mean parameter estimates are used to solve the FEM model and the results are compared against the results obtained for “true” parameter values. A sensitivity study is also conducted to demonstrate methods for properly formulating model spaces to improve the Bayesian estimation routine. The study concludes with a discussion highlighting factors that need to be considered when leveraging experimental data to update FEMs of concrete structures using AI techniques. 

   more » « less
5. Ecological Impact of Green Computing Using Graphical Processing Units in Molecular Dynamics Simulations

   https://doi.org/10.4018/IJGC.2018010103  

   Marquetti, Izabele; Rodrigues, Jhonatam; Desai, Salil S. (January 2018, International Journal of Green Computing)

   Molecular dynamics (MD) models require comprehensive computational power to simulate nanoscale phenomena. Traditionally, central processing unit (CPU) clusters have been the standard method of performing these numerically intensive computations. This article investigates the use of graphical processing units (GPUs) to implement large-scale MD models for exploring nanofluidic-substrate interactions. MD models of water nanodroplets over flat silicon substrate are tracked wherein the simulation attains a steady state computational performance. Different classes of GPU units from NVIDIA (C2050, K20, and K40) are evaluated for energy efficiency performance with respect to three green computing measures: simulation completion time, power consumption, and CO2 emissions. The CPU+K40 configuration displayed the lowest energy consumption profile for all the measures. This research demonstrates the use of energy efficient graphical computing versus traditional CPU computing for high-performance molecular dynamics simulations. 

   more » « less