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Undertaking an experience-sampling study via smartphones is complex. Scheduling and sending mobile notifications often requires the use of proprietary software that imposes limits on participants' operating systems (whether iOS or Android) or the types of questions that can be asked via the application. We have developed an open-source platform—Samply—which overcomes these limitations. Researchers can access the entire interface via a browser, manage studies, schedule and send notifications linking to online surveys or experiments created in any Internet-based service or software, and monitor participants' responses—all without the coding skills usually needed to program a native mobile application. Participants can download the Samply Research mobile application for free from Google Play or the App Store, join a specific study, receive notifications and web links to surveys or experiments, and track their involvement. The mobile application leverages the power of the React Native JavaScript library, which allows it to be rendered in the native code of Android and iOS mobile operating systems. We describe Samply, provide a step-by-step example of conducting an experience-sampling study, and present the results of two validation studies. Study 1 demonstrates how we improved the website's usability for researchers. Study 2 validates the mobile application's data recording ability by analyzing a survey's participation rate. The application's possible limitations and how mobile device settings might affect its reliability are discussed. [ABSTRACT FROM AUTHOR]

In this study, a simple and customizable convolution neural network framework was used to train a vibration classification model that can be integrated into the measurement application in order to realize accurate and real-time bridge vibration status on mobile platforms. The inputs for the network model are basically the multichannel time-series signals acquired from the built-in accelerometer sensor of smartphones, while the outputs are the predefined vibration categories. To verify the effectiveness of the proposed framework, data collected from long-term monitoring of bridge were used for training a model, and its classification performance was evaluated on the test set constituting the data collected from the same bridge but not used previously for training. An iOS application program was developed on the smartphone for incorporating the trained model with predefined classification labels so that it can classify vibration datasets measured on any other bridges in real-time. The results justify the practical feasibility of using a low-latency, high-accuracy smartphone-based system amid which bottlenecks of processing large amounts of data will be eliminated, and stable observation of structural conditions can be promoted. [ABSTRACT FROM AUTHOR]