Nanoparticle strips (NPS) are widely used as external reinforcers for two-way reinforced concrete slabs. However, the Structural Health Monitoring (SHM) of these slabs is a very important issue and was evaluated in this study. This study has been done analytically and numerically to optimize the placement of sensors. The properties of slabs and carbon nanotubes as composite sheets were considered isotopic and orthotropic, respectively. The nonlinear Finite Element Method (FEM) approach and suitable optimal placement of sensor approach were developed as a new MATLAB toolbox called DECOMAC by the authors of this paper. The Suitable multi-objective function was considered in optimized processes based on distributed ECOMAC method. Some common concrete slabs in construction with different aspect ratios were considered as case studies. The dimension and distance of nano strips in retrofitting process were selected according to building codes. The results of Optimal Sensor Placement (OSP) by DECOMAC algorithm on un-retrofitted and retrofitted slabs were compared. The statistical analysis according to the Mann-Whitney criteria shows that there is a significant difference between them (mean P-value = 0.61).

Ensuring the safety of a structure necessitates that repairs are carried out based on accurate inspections and records of damage information. Traditional methods of recording damage rely on individual paper-based documents, making it challenging for inspectors to accurately record damage locations and track chronological changes. Recent research has suggested the adoption of building information modeling (BIM) to record detailed damage information; however, localizing damages on a BIM model can be time-consuming. To overcome this limitation, this study proposes a method to automatically localize damages on a BIM model in real-time, utilizing consecutive images and measurements from an inertial measurement unit in close proximity to damages. The proposed method employs a visual-inertial odometry algorithm to estimate the camera pose, detect damages, and compute the damage location in the coordinate of a prebuilt BIM model. The feasibility and effectiveness of the proposed method were validated through an experiment conducted on a campus building. Results revealed that the proposed method successfully localized damages on the BIM model in real-time, with a root mean square error of 6.6 cm.

Although unmanned aerial vehicles have been used to overcome the limited accessibility of human-based visual inspection, unresolved issues still remain. Onsite inspectors face difficulty finding previously detected damage locations and tracking their status onsite. For example, an inspector still marks the damage location on a target structure with chalk or drawings while comparing the current status of existing damages to their previous status, as documented onsite. In this study, an augmented-reality-based structural inspection system with onsite damage information marking was developed to enhance the convenience of inspectors. The developed system detects structural damage, creates a holographic marker with damage information on the actual physical damage, and displays the marker onsite via an augmented reality headset. Because inspectors can view a marker with damage information in real time on the display, they can easily identify where the previous damage has occurred and whether the size of the damage is increasing. The performance of the developed system was validated through a field test, demonstrating that the system can enhance convenience by accelerating the inspector's essential tasks such as detecting damages, measuring their size, manually recording their information, and locating previous damages.

Field monitoring techniques offer an attractive approach for understanding bridge behavior under in-service loads. However, the investigations on bridge behavior under high-speed train load using field monitoring data are limited. The focus of this study is to explore the structural behavior of an in-service long-span steel truss arch bridge based on field monitoring data. First, the natural frequencies of the structure, as well as the train driving frequencies, are extracted. Then, the train-induced bearing displacement and structural strain are explored to identify the effects of train loads and bearings. Subsequently, a sensitivity analysis is performed for the impact factor of strain responses with respect to the train speed, train weight, and temperature to identify the fundamental issues affecting these responses. Additionally, a similar sensitivity analysis is conducted for the peak acceleration. The results indicate that the friction force in bearings provides residual deformations when two consecutive trains are in opposite directions. In addition, the impact factor and peak acceleration are primarily affected by train speed, particularly near train speeds that result in the resonance of the bridge response. The results can provide additional insight into the behavior of the long-span steel truss bridges under in-service high-speed train loads.

In this study, the accuracy of a real-time hybrid test (RTHT) employed for a performance test of a tuned mass damper (TMD) on an offshore wind turbine (OWT) with a complicated jacket-type supporting structure is quantified and evaluated by comparing the RTHT results with the experimental data obtained from a shaking table test (STT), in which a 1/25-scale model for a typical 5-MW OWT controlled by a TMD was tested. In the RTHT, the jacket-type OWT structure was modelled using both multiple-DOF (MDOF) and single-DOF (SDOF) numerical models. When compared with the STT test data, the test results of the RTHT show that while the SDOF model, which requires less control computational time, is able to well predict the peak responses of the nacelle and TMD only, the MDOF model is able to effectively predict both the peak and over-all time-history responses at multiple critical locations of an OWT structure. This also indicates that, depending on the type of structural responses considered, an RTHT with either an SDOF or a MDOF model may be a promising alternative to the STT to assess the effectiveness of a TMD for seismic mitigation in an OWT context.