A new damage quantification approach for shear-wall buildings using ambient vibration data

This paper presents a new approach to estimate damage seventy tor slaear-wall DUlltllngs using alagona~ terms of a modal flexibility matrix estimated from dynamic properties. This study aims to provide a fundamental concept for quantifying the damage of realistic buildings by investigating an idealized shear-wall building. Numerical studies were performed on a 5-story shear-wall building model to validate the applicability of the presented approach, using two damage patterns. With the numerical simulations, the proposed approach accurately determined the damage ratio of the specimens. Experiments were also conducted on a 5-story shear-wall building model for which the system parameters were almost the same as those in numerical simulations. The estimated damage-quantification results from the experimental validations demonstrated that the performance of the presented method for shear-wall buildings was both suitable and accurate.

DAMAGE DETECTION IN BUILDINGS USING A TWO-STAGE SENSITIVITY-BASED METHOD FROM MODAL TEST DATA

Many multi-story or highrise buildings consisting of a number of identical stories are usually considered as periodic spring-mass systems. The general expressions of natural frequencies, mode shapes, slopes and curvatures of mode shapes of the periodic spring-mass system by utilizing the periodic structure theory are derived in this paper. The sensitivities of these mode parameters with respect to structural damages, which do not depend on the physical parameters of the original structures, are obtained. Based on the sensitivity analysis of these mode parameters, a two-stage method is proposed to localize and quantify damages of multi-story or highrise buildings. The slopes and curvatures of mode shapes, which are highly sensitive to local damages, are used to localize the damages. Subsequently, the limited measured natural frequencies, which have a better accuracy than the other mode parameters, are used to quantify the extent of damages within the potential damaged locations. The experimental results of a 3-story experimental building demonstrate that the single or multiple damages of buildings, either slight or severe, can be correctly localized by using only the slope or curvature of mode shape in one of the lower modes, in which the change of natural frequency is the largest, and can be accurately quantified by the limited measured natural frequencies with noise pollution.

Defect inspection of indoor components in buildings using deep learning object detection and augmented reality

Visual inspection is commonly adopted for building operation,maintenance,and safety.The durability and defects of components or materials in buildings can be quickly assessed through visual inspection.However,implementations of visual inspection are substantially time-consuming,labor-intensive,and error-prone because useful auxiliary tools that can instantly highlight defects or damage locations from images are not available.Therefore,an advanced building inspection framework is developed and implemented with augmented reality(AR)and real-time damage detection in this study.In this framework,engineers should walk around and film every corner of the building interior to generate the three-dimensional(3D)environment through ARKit.Meanwhile,a trained YOLOv5 model real-time detects defects during this process,even in a large-scale field,and the defect locations indicating the detected defects are then marked in this 3D environment.The defects areas can be measured with centimeter-level accuracy with the light detection and ranging(LiDAR)on devices.All required damage information,including defect positions and sizes,is collected at a time and can be rendered in the 2D and 3D views.Finally,this visual inspection can be efficiently conducted,and the previously generated environment can also be loaded to re-localize existing defect marks for future maintenance and change observation.Moreover,the proposed framework is also implemented and verified by an underground parking lot in a building to detect and quantify surface defects on concrete components.As seen in the results,the conventional building inspection is significantly improved with the aid of the proposed framework in terms of damage localization,damage quantification,and inspection efficiency.