Traditional methods for seismic damage evaluation require manual extractions of intensity measures (IMs) to properly represent the record-to-record variation of ground motions. Contemporary methods such as convolutional neural networks (CNNs) for time series classification and seismic damage evaluation face a challenge in training due to a huge task of ground-motion image encoding. Presently, no consensus has been reached on the understanding of the most suitable encoding technique and image size (width × height × channel) for CNN-based seismic damage evaluation. In this study, we propose and develop a new image encoding technique based on time-series segmentation (TS) to transform acceleration (A), velocity (V), and displacement (D) ground motion records into a three-channel AVD image of the ground motion event with a pre-defined size of width × height. The proposed TS technique is compared with two time-series image encoding techniques, namely recurrence plot (RP) and wavelet transform (WT). The CNN trained through the TS technique is also compared with the IM-based machine learning approach. The CNN-based feature extraction has comparable classification performance to the IM-based approach. WT 1,000 × 100 results in the highest 79.5% accuracy in classification while TS 100 × 100 with a classification accuracy of 76.8% is most computationally efficient. Both the WT 1,000 × 100 and TS 100 × 100 three-channel AVD image encoding methods are promising for future studies of CNN-based seismic damage evaluation.
Contemporary deep learning approaches for post-earthquake damage assessments based on 2D convolutional neural networks (CNNs) require encoding of ground motion records to transform their inherent 1D time series to 2D images, thus requiring high computing time and resources. This study develops a 1D CNN model to avoid the costly 2D image encoding. The 1D CNN model is compared with a 2D CNN model with wavelet transform encoding and a feedforward neural network (FNN) model to evaluate prediction performance and computational efficiency. A case study of a benchmark reinforced concrete (r/c) building indicated that the 1D CNN model achieved a prediction accuracy of 81.0%, which was very close to the 81.6% prediction accuracy of the 2D CNN model and much higher than the 70.8% prediction accuracy of the FNN model. At the same time, the 1D CNN model reduced computing time by more than 90% and reduced resources used by more than 69%, as compared to the 2D CNN model. Therefore, the developed 1D CNN model is recommended for rapid and accurate resultant damage assessment after earthquakes.
Machine learning and computational intelligence have facilitated the development of recommendation systems for a broad range of domains. Such recommendations are based on contextual information that is explicitly provided or pervasively collected. Recommendation systems often improve decision‐making or increase the efficacy of a task. Real‐time strategy (RTS) video games are not only a popular entertainment medium, they also are an abstraction of many real‐world applications where the aim is to increase your resources and decrease those of your opponent. Using predictive analytics, which examines past examples of success and failure, we can learn how to predict positive outcomes for such scenarios. The goal of our research is to develop an accurate predictive recommendation system for multiplayer strategic games to determine recommendations for moves that a player should, and should not, make and thereby provide a competitive advantage. Herein we compare two techniques, frequent and discriminative subgraph mining, in terms of the error rates associated with their predictions in this context. As proof of concept, we present the results of an experiment that utilizes our strategies for two particular RTS games. This article is categorized under: Application Areas > Data Mining Software Tools Technologies > Prediction Commercial, Legal, and Ethical Issues > Fairness in Data Mining
The reproducibility of scientific findings is essential to the integrity of research. The scientific method requires hypotheses to be validated independently by different laboratories. Investigators are expected to provide sufficient information in their publications to permit an objective evaluation of their methods and an independent reproduction of their results. This is particularly true for research supported by public funds, where transparency of both methods and findings represents a return on public investment. Unfortunately, many publications fall short of this standard for various reasons, including a desire to protect intellectual property or national security. The reproducibility of findings is essential in transferring machine learning findings from research into healthcare practice. Fortunately, the internet makes it easier to overcome these limitations by permitting multiple individuals to participate in reproducibility efforts and to crowdsource the reverse engineering of novel software. We present a case study of this capability from neural network research. The success of the crowdsourced project Leela Zero to reverse engineer the findings of AlphaGo Zero exemplifies the ability to reproduce novel results despite the lack of extensive computational resources or a detailed description of the initial experimental methods. The implications of this successful reverse engineering effort for future reproducibility of neural network research are discussed.
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