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Interstory drift response is one of most important quantities to quickly assess performance and damages in buildings. Nevertheless, direct measurement of interstory drift is difficult and expensive, because a stationary reference is required to attach measurement devices. With the goal of accurate and fast reference-free estimation, this paper proposes a new strategy to determine dynamic interstory drifts using accelerations. In particular, a Tikhonov regularization is adopted in a generalized minimization problem to achieve an efficient and stable finite impulse response (FIR) filter. Furthermore, due to independent clocks in wireless sensors, accurate time synchronization of the records is critical; consequently, a strategy for accurate synchronization is also presented. Finally, the proposed strategy has been deployed on edge devices for onboard real-time interstory drift estimation. The proposed method for dynamic interstory drift estimation is validated, first, by numerical simulation using earthquake records as base excitation of linear and nonlinear buildings, as well as through laboratory shake table experiments. Both numerical and lab test results show good agreement of dynamic interstory drifts between the measured value and estimated results, demonstrating the efficacy of the proposed method to estimate the dynamic displacements of seismically excited structures. [ABSTRACT FROM AUTHOR]

This paper presents a smart artificial neural network (ANN)-based slip-trip classification method, which integrates a smart sensor and an ANN. It was trained to identify the slip and trip events that occur while a worker walks in a workplace. It encourages preventive and collective actions to reduce construction accidents by identifying the type of near miss, i.e., slip or trip, and the exact time that it occurs. The variation in the energy released by a worker is measured using a triaxial accelerometer embedded in a smart phone. This study is of value to researchers because the method measures a near miss quantitatively using acceleration. It is also of relevance to practitioners because it provides a computerized tool that records each and every moment of a near-miss event. A test was performed by collecting the three-axis acceleration streams generated by workers wearing a smart phone running the classifier as they walked around a simulated construction jobsite. It identified the type of near miss and the exact time of its occurrence. The test case verified the usability and validity of the computational methods. [ABSTRACT FROM AUTHOR]