The seismic risk mitigation plans are vital since vulnerable structures are prone to partial or total collapse under the effect of future major earthquake events. Therefore, vulnerable structures in large building stocks should be determined using robust and accurate methods to prevent loss of lives and property. In the current state‐of‐the‐art, the risk states (i.e., whether risky or not) of structures completely depend on the experience of the reconnaissance team of engineers, which could not result in standardized decisions. In this study, machine learning has been integrated into the decision‐making algorithm to classify more precise and reliable seismic risk states of masonry buildings, categorizing them into up to four risk categories. For this purpose, a large database, including 12 features and detailed seismic risk analysis results of 4356 masonry buildings, is formed. Firstly, the input variables are preprocessed using feature engineering methods. Then, several machine learning algorithms are utilized to produce a network to estimate the risk state of masonry buildings in association with the risk states obtained from the detailed analysis results. As a result of the analysis of these algorithms, the correct prediction percentages for the testing database of the proposed method for two, three, and four risk states classification are predicted as approximately 87.5%, 86.6%, and 79.0%, respectively. This new approach makes it possible to produce risk color maps of large building stocks and reduce the number of buildings that require immediate action.

Precise, seamless, and high-rate navigation remains a major challenge, particularly when relying on low-cost sensors. With the decreasing cost of cameras, Inertial Measurement Units (IMUs), and Global Navigation Satellite System (GNSS) receivers, tightly coupled fusion frameworks, such as GVINS, have gained considerable attention. GVINS is an optimization-based factor-graph framework that integrates visual and inertial measurements with single-frequency GNSS-code pseudorange observations to provide robust and drift-free navigation. This study aimed to evaluate the potential of applying GVINS to low-cost, low-power, and single-frequency GNSS receivers, particularly those embedded in smartphones, by integrating 1 Hz GNSS measurements collected in three challenging urban scenarios into the GVINS framework to produce seamless 10 Hz positioning estimates. The experiments were conducted using an Xsens MTi-1 IMU and global-shutter (GS) cameras, as well as a Samsung A51 smartphone and a u-blox ZED-F9P GNSS receiver. GVINS was modified to process 1 Hz GNSS measurements. Differential corrections from a nearby GNSS reference station were also incorporated to assess their impact on optimization-based filters, such as GVINS. The performance of GVINS and Differential GVINS (D-GVINS) solutions using smartphone measurements was compared against standard point positioning (SPP) and differential GPS (DGPS) results obtained from the same smartphone GNSS receiver, as well as the GVINS solution derived from u-blox ZED-F9P measurements sampled at 1 Hz. Experimental results show that GVINS effectively operates with smartphone GNSS measurements, reducing 3D RMS errors by 80.4%, 64.9%, and 83.8% for the sports field, campus-walking, and campus-driving datasets, respectively, when differential corrections are applied relative to the SPP solution. These results highlight the potential of smartphone GNSS receivers within the GVINS framework: Even though they observe fewer constellations, lower signal quality, and a lower number of satellites, they can still achieve a performance comparable to that of a relatively higher-end dual-frequency GNSS receiver, the u-blox ZED-F9P. Further studies will focus on adapting the GVINS algorithm to run directly on smartphones to utilize all the available measurements, including the camera, IMU, barometer, magnetometer, and additional ranging sensors.