Two different nondestructive test (NDT) methods, air-coupled impact-echo (IE) and infrared (IR) thermography are evaluated on a full-scale simulated reinforced concrete bridge deck containing simulated delamination and cracking defects. The IE data are presented as two-dimensional frequency maps and spectral B-scan lines. The IR data are presented as temperature maps on the concrete surface. The lateral boundaries of the detected delaminations are also indicated in the images. The results obtained from each of the individual NDT methods show reasonably good agreement with most of the actual defects. The advantages and limitations of each method to characterize defects are discussed. The consistency and sensitivity of each method are also investigated. Finally, a simple data fusion technique is proposed to improve effectiveness of the individual test data. The findings from this study demonstrate that the combination of air-coupled IE and IR thermography tests is a practical option for consistent a...

The field application of three different nondestructive tests (NDTs)—air-coupled impact echo (IE), infrared (IR) thermography, and sounding (chain drag)—are evaluated in this paper, where an actual in-service concrete bridge deck is tested. Two different contactless IE test equipment sets are deployed as part of an effort to develop new rapid measurement methods. The IE data are presented as two-dimensional frequency maps, and the IR data are presented as temperature maps over the tested area. Sounding (chain-drag) result maps are also presented. For verification of the location of near-surface delamination damage, eight drilled core samples were extracted from the test area. The results obtained from each of the individual NDT methods show reasonably good agreement with the drilled cores in terms of locating near-surface delamination. Finally, the NDT methods are compared across general performance criteria, considering accuracy, testing practicality, and costs. The analysis shows that all of the...

A terrestrial light detection and ranging (LiDAR) can be used to construct the building information modeling as well as to measure shape deformation that varies with time or load during or after construction. Thus, the measurement can be utilized for risk management and the completion inspection during and after construction, and for life cycling cost analysis to decide the reconstruction of existing structure. In this regard, this paper presents a practical feasibility study of a shape information model to monitor deformation or deflection of bridge structures with data loss minimization and computational efficiency. The 3D position information of the structure was obtained using terrestrial laser scanning, and the octree data structure was used for efficient processing of the acquired large-scale scan data. To accomplish this, we constructed a shape information model which means a 3D visualization process on the basis of the octree algorithm, which converts point cloud data via voxel to the improved octree structure by efficient management of empty nodes. First, laser scanning was carried out on a steel box-girder bridge (Bridge 1) and a steel I-girder bridge (Bridge 2), and the data for $35.4\times 29.2\times 6.89{\text{m}}^3$ and $9.3\times 5.4\times 1.8{\mathrm{m}}^3$ in each test-bed area were compressed from 158.72 to 13.05 MB with 91.7% reduction for the Bridge 1 and from 21.5 to 2.3 MB with 89.3% for the Bridge 2. As a next step, the shape information model was applied to the deflection estimation of another steel box girder bridge in service (Bridge 3) associated with the octree space division. The results showed the reasonable deflection values in comparison with the LVDT measurement. The proposed approach can provide useful information for effective condition evaluation and maintenance of bridge structures.

Mechanical waves, such as ultrasonic waves, have shown promise for use in non-destructive methods used in the evaluation of concrete properties, such as strength and elasticity. However, accurate estimation of the concrete compressive strength is difficult if only the pressure waves (P-waves) are considered, which is common in non-destructive methods. P-waves cannot reflect various factors such as the types of aggregates and cement, the fine aggregate modulus, and the interfacial transition zone, influencing the concrete strength. In this study, shear waves (S-waves) and Rayleigh waves (R-waves) were additionally used to obtain a more accurate prediction of the concrete strength. The velocities of three types of mechanical waves were measured by recent ultrasonic testing methods. Two machine learning models—a support vector machine (SVM) and an artificial neural network (ANN)—were developed within the MATLAB programming environment. Both models were successfully used to model the relationship between the mechanical wave velocities and the concrete compressive strength. The machine learning model that included the P-, S-, and R-wave velocities was more accurate than the model that included only the P-wave velocity.

During concrete pumping, the migration and redistribution of particles occur in a pipe and the lubrication layer that forms between the bulk concrete and the pipe wall is the governing factor determining the flow behavior. In order to identify flow behavior of pumping, in this study, the viscoelastic properties related to the microstructural behavior of a flocculated suspension were examined by using dynamic oscillatory measurements. Cement paste is assumed to be a constituent material of the lubrication layer and ten cases of mixing design are employed by changing the proportions of mineral admixtures. The relationship between the yield stress obtained from the steady shear test and the dynamic modulus resulted from the oscillatory shear measurement was derived and the implications of the correlation are discussed. Moreover, based on the investigation of the viscoelastic properties with oscillatory measurements, the initial behavior of pumped concrete was analyzed systematically.