In-service Bridge Research Articles

Longitudinal Multiple Presence of Trucks on Continuous Bridge Spans

In designing and evaluating roadway bridges in the surface transportation infrastructure, truck load is a major variable to account for. For daily operation, truck load also is a primary factor. Trucks on continuous spans were studied, with a focus on their behavior of longitudinal multiple presence in the same lane(s). Current US specifications include provisions for how to account for such multiple presence, and these provisions have evolved over the years. However, the basis for the provisions and their changes have not been well documented. An unprecedentedly large number of high-quality in-motion truck weight data were gathered in eight states across the US and used to investigate trucks’ longitudinal multiple presence behavior. A new approach to analyzing each platoon of trucks recorded in traffic was developed for this purpose. Each random platoon’s total load effect was identified, covering every truck in the platoon, to obtain reliable statistics of the maximum load effects. Results show that longitudinal multiple presence increases with truck volume and span length. Relevant current AASHTO provisions therefore were found to be conservative or overconservative. Accordingly, it was concluded that lower longitudinal multiple presence factor values for evaluation and design are more justifiable. These results may be used by bridge owners and engineers for realistic load rating and design, for more-uniform structural safety and cost effectiveness. This is consistent with the top two goals of the AASHTO Committee on Bridges and Structures’ strategic plan for extending bridge service life and assessing bridge condition.

Vibration Influence on Structural Perspectives According to Pedestrian Traffic Type of Pedestrian Bridge

The vibration acceleration response and excellent frequency generated by pedestrian’s walking type of the steel composite wooden bridge were measured and analyzed. Based on the modified Reiher-Meister vibration curve, vibration influence evaluation on the structural perspectives of the pedestrian bridge was performed. The pedestrian’s walking types were divided into walking, running, rolling, and shaking, and eight experimental cases were set up by combining them with the number of users and moving direction. A total of 37 measurements were made. To evaluate the vibration serviceability of pedestrian bridges, the pedestrian’s walking type must be considered, and a standardized study on the pedestrian’s walking type will be necessary in the future.

Estimation of Water Stagnation in Asphalt-Overlaid Bridges Using Ground-Penetrating Radar

Stagnant water in asphalt-overlaid bridge decks is a primary cause of deterioration. Rainwater seeping through the asphalt layer stagnates on waterproofing membranes of the bridge deck, consequently degrading the asphalt pavement and the underlying concrete deck. Thus, identifying ponding regions under pavements potentially containing water can facilitate the prognostic maintenance of bridge decks. This study proposes a framework to estimate the subsurface ponding zone in bridge decks using ground-penetrating radar (GPR). The depth distribution of the nonpermeable layer in the subsurface of the bridge is extracted (depth map) from the GPR C-scan using a conventional thickness evaluation method and used to build a bathymetric dendrogram to model subsurface water flows. The subsurface ponding zone can be identified by considering drainage on the bathymetric dendrogram. The proposed framework is demonstrated using an in-service bridge in Korea. The estimated subsurface ponding zone is compared with damage locations of concrete observed after hydrodemolition.

Bridge Deformation Measurement Using Unmanned Aerial Dual Camera and Learning-Based Tracking Method

Bridge deformation response data are the basis for calculating the dynamic parameters of the bridge, and it is of great significance to accurately measure the deformation response of the bridge during the load test and service conditions. A bridge deformation measurement method using an unmanned aerial system (UAS) with dual cameras and a deep learning-based object tracking method is proposed to measure the bridge deformation. The contributions are as follows: (1) To address the problem that the movement of the UAS brings error to the deformation measurement results, dual cameras with telephoto and wide-angle lenses are used to simultaneously capture the deformed points and stable points on the bridge, so as to simultaneously measure the deformation of the bridge and the displacement of the UAS, and then the displacement of UAS is eliminated by using the homography relationship between the two cameras. (2) To solve the problem that the traditional digital image correlation-based displacement measurement method is easily disturbed by factors such as light changes and occlusion, a displacement calculation method based on object detection network and target tracking algorithm is proposed to achieve the stable target displacement measurement. Finally, the proposed method was verified in a laboratory test and applied to the deformation measurement of an in-service bridge to verify the practicability of the proposed method.

Establishment and evaluation of conditional GAN-based image dataset for semantic segmentation of structural cracks

Crack detection is vital to maintain the structural safety of in-service bridges, which is an increasing demand in the industrial community. The deep learning-based crack detection is an emerging method that provides a novel way to deal with this problem. Although, the deep neural networks (DNN) can learn to detect cracks themselves, they require large numbers of crack images to learn the features of cracks in the real world. Besides, except for the crack forms, there always exist all kinds of noise motifs that will disturb the correct detection of crack regions. The lack of crack sample images has been an obstacle for the improvement of deep learning-based crack detection method. This paper proposes a generative adversarial network (GAN) based method to establish a synthesized crack image dataset with pixel-wise annotations, which provides a novel way aside from the traditional data augmentation method. The Deep Convolutional GAN (DCGAN) model was adopted for the generation of synthesized crack annotations while the Pixel2Pixel model was used to generate the corresponding synthesized crack images. The generated crack annotations and crack images during the training epochs were demonstrated to show how the GANs learn to generate the synthesized images. Moreover, comparative study was conducted to validate the performance of the synthesized crack image dataset for training the crack detection DNN. The results showed the DNN trained by the synthesized images can achieve 74.34% of the MeanIoU that was reached by the same DNN model trained with real images. As for the way of using the synthesized and real crack images for training a crack detection DNN, the way that use the synthesized crack images for pre-training and then use the real images for fine-tuning is better than the way that directly mix the synthesized and real crack images for training. This work provides reference for the GAN-based establishment of crack image dataset and the evaluation of the image quality for training crack detection DNNs.

An Improved Structural Displacement Monitoring Approach by Acceleration-Aided Tilt Camera Measurement

Computer vision is becoming one of the most popular remote-sensing techniques and has been used widely in displacement monitoring and damage identification of in-service bridges. Nevertheless, several obstacles, including limited sampling rate, insufficient resolution for remote measurement, and error induced by camera tilting, restrict the application of vision-based approaches in structural health monitoring (SHM). The combination of a traditional SHM system and a modern remote-sensing technique can significantly improve the accuracy and reliability of the monitoring system. To make full use of data collected in the traditional SHM system and computer vision technique and overcome their shortcomings, we presented an improved bridge displacement estimation approach for SHM purposes by fusing camera-based and acceleration measurements. First, we estimated the scaling factor, which transfers pixel displacement to real displacement under tilt photogrammetry, by the acceleration reconstructed and camera-based displacements in the same frequency band without the actual size of the structure or the measurement parameters. Then, we extracted the low-frequency displacement from the vision-based measurement, and we fused the high-frequency displacement that was reconstructed from the acceleration measurement to achieve high-accuracy displacement estimation. The efficiency of this method was validated through dynamic load tests on a suspension model bridge in the laboratory and field tests on a highway and subway cable-stayed bridge.

Missing Structural Health Monitoring Data Recovery Based on Bayesian Matrix Factorization

The exposure of bridge health-monitoring systems to extreme conditions often results in missing data, which constrains the health monitoring system from working. Therefore, there is an urgent need for an efficient data cleaning method. With the development of big data and machine-learning techniques, several methods for missing-data recovery have emerged. However, optimization-based methods may experience overfitting and demand extensive tuning of parameters, and trained models may still have substantial errors when applied to unseen datasets. Furthermore, many methods can only process monitoring data from a single sensor at a time, so the spatiotemporal dependence among monitoring data from different sensors cannot be extracted to recover missing data. Monitoring data from multiple sensors can be organized in the form of matrix. Therefore, matrix factorization is an appropriate way to handle monitoring data. To this end, a hierarchical probabilistic model for matrix factorization is formulated under a fully Bayesian framework by incorporating a sparsity-inducing prior over spatiotemporal factors. The spatiotemporal dependence is modeled to reconstruct the monitoring data matrix to achieve the missing-data recovery. Through experiments using continuous monitoring data of an in-service bridge, the proposed method shows good performance of missing-data recovery. Furthermore, the effect of missing data on the preset rank of matrix is also investigated. The results show that the model can achieve higher accuracy of missing-data recovery with higher preset rank under the same case of missing data.

Cyclic performance of severely earthquake-damaged RC bridge columns repaired using grouted UHPC jacket

The bridges constructed in high seismic zones may suffer one or more earthquake shocks of certain scales during their service life. Some of them could be severely damaged during a catastrophic earthquake event. How to quickly and economically restore bridge service functions and traffic lifelines has always been the concern of bridge owners and engineers. In this study, a method for repairing seismically damaged reinforcement concrete (RC) bridge columns using ultrahigh performance concrete (UHPC) materials was developed. Seven down-scale RC bridge column specimens with three different reinforcement ratios and two different heights were designed according to the seismic design code for RC bridges. For three of the specimens, the reinforcement around the plastic hinge area were artificially corroded through chloride ion penetration method to simulate the strength degradation effect of reinforcement erosion that could occur in many aging bridge columns. All seven specimens were tested by applying cyclic loads until their plastic hinge became severely damaged, which were then repaired following the proposed procedure and with different jacket thicknesses. The cyclic tests were re-conducted for these repaired specimens. The testing results were analyzed by comparing to those from the test for the non-seismic-damaging specimens one by one with respect to their seismic performance in order to validate the seismic rehabilitation effect using UHPC materials. Additionally, an ABAQUS software based finite element model was developed for numerically simulating the hysteretic behaviors of the columns repaired using UHPC jacket. The resulting hysteretic curves for the repaired columns with different reinforcement ratios and heights well matched those from the corresponding cyclic tests. This model can be further used for seismic performance prediction and UHPC jacket design for the repaired bridge columns that have different dimensions and reinforcement configurations. All of these experimental and analytical results show that the seismic performance of the repaired bridge columns can be effectively restored in a relatively fast and low-cost manner.

A Framework for Evaluating the Load-Carrying Capacity of Bridges without Design Document Using an AI Technique

As the number of old bridges increases worldwide, economic and maintenance issues are emerging due to the deterioration of these structures. In general, the conventional approach for the safety assessment of existing bridges is based on performing structural analysis and safety verifications, starting from the material properties obtained from experimental tests. In particular, for some old bridges, the design documents are not computerized or stored, so many additional field tests may be required due to the uncertainty of information. In this paper, we proposed a framework that can estimate the load-carrying capacity of old bridges for which the design documents are absent, and field tests are not used in this process. The framework relies on computational design strength and features procedures for calculating calibration factors to reflect the current conditions. With only limited information available with regard to bridges, the key to this study is its use of AI technology. First, the relationship between externally measurable geometric characteristics and the design strength was established based on 124 design documents. In this process, we compared the performance of five regression algorithms: multiple linear regression (MLR), decision tree (DT), boosting tree (BT), support vector machine (SVM), and Gaussian process regression (GPR). It was confirmed that it is possible to predict the design strength using GPR, with an error rate of 0.3%. Second, an ANN model was built to estimate the calibration factor as a condition assessment of 82 in-service bridges. The ANN was determined using optimal parameters with a mean squared error (MSE) of 0.008. Each type of AI used in the proposed framework showed a high predictive performance, implying that it can be used to evaluate the load-carrying capacity of bridges without a design document.

Handling policies for Italian existing bridges with a territorial approach: the case study of Caserta, Italy

The present manuscript deals with the huge problem related to the structural safety of the Italian infrastructural asset. The case study of the Province of Caserta, whose infrastructural heritage includes about 900 in-service bridges mainly realized in the second half of the last century, is presented and addressed. In this territorial context, a wide handling activity started since 2017, including: i. census of all structures; ii. definition of a priority scale for programming in-situ inspections; iii. visual inspections aimed at estimating the conservation state of the structures and identifying structural issues; and iv. definition of provisions for the structural safety of existing bridges. Hence, the investigated sample is presented, firstly. Then, given the large number of structures of interest, an empirical methodology for defining a priority scale for the in-situ surveys is proposed. Based on such a procedure, the outcome of the inspections related to 60 bridges are analyzed and discussed and the main structural issues affecting the considered bridges highlighted.

Automatic Inspection of Bridge Bolts Using Unmanned Aerial Vision and Adaptive Scale Unification-Based Deep Learning

Bolted connections are essential components that require regular inspection to ensure bridge safety. Existing methods mainly rely on traditional artificial vision-based inspection, which is inefficient due to the many bolts of bridges. A vision-based method using deep learning and unmanned aerial vision is proposed to automatically analyze the bridge bolts’ condition. The contributions are as follows: (1) Addressing the problems that motion blur often exists in videos captured by unmanned ariel systems (UASs) with high moving speed, and that bolt damage is hard to accurately detect due to the few pixels a single bolt occupies, a bolt image preprocessing method, including image deblurring based on inverse filtering with camera motion model and adaptive scaling based on super-resolution, is proposed to eliminate the motion blur of bolt images and segment them into subimages with uniform bolt size. (2) Addressing the problem that directly applying an object detection network for both bolt detection and classification may lead to the wrong identification of bolt damage, a two-stage detection method is proposed to divide bolt inspection into bolt object segmentation and damage classification. The proposed method was verified on an in-service bridge to detect bolts and classify them into normal bolts, corrosion bolts, and loose bolts. The results show that the proposed method can effectively eliminate the inherent defects of data acquired by UAS and accurately classify the bolt defects, verifying the practicability and high precision of the proposed method.

Sensor Equipped UAS for Non-Contact Bridge Inspections: Field Application

In the future, sensors mounted on uncrewed aerial systems (UASs) will play a critical role in increasing both the speed and safety of structural inspections. Environmental and safety concerns make structural inspections and maintenance challenging when conducted using traditional methods, especially for large structures. The methods developed and tested in the laboratory need to be tested in the field on real-size structures to identify their potential for full implementation. This paper presents results from a full-scale field implementation of a novel sensor equipped with UAS to measure non-contact transverse displacement from a pedestrian bridge. To this end, the authors modified and upgraded a low-cost system that previously showed promise in laboratory and small-scale outdoor settings so that it could be tested on an in-service bridge. The upgraded UAS system uses a commodity drone platform, low-cost sensors including a laser range-finder, and a computer vision-based algorithm with the aim of measuring bridge displacements under load indicative of structural problems. The aim of this research is to alleviate the costs and challenges associated with sensor attachment in bridge inspections and deliver the first prototype of a UAS-based non-contact out-of-plane displacement measurement. This work helps to define the capabilities and limitations of the proposed low-cost system in obtaining non-contact transverse displacement in outdoor experiments.

Application of unsupervised learning for post-earthquake assessment of the Z24 benchmark bridge

Due to the significant number of ageing infrastructures, earthquake-induced damage assessment plays a fundamental role to mitigate the seismic risk, while enabling proper preventive maintenance and providing bridge serviceability and safety. In the wake of recent advancements in Artificial Intelligence and computational power, this paper presents a fully-automated Machine Learning (ML)-based technique for unsupervised damage detection in monitored reinforced concrete (RC) bridges. The innovative methodology is based on the autoencoder, a feed-forward neural network with the ability to reconstruct a given input from its low-dimensional representation. Considering this assumption, healthy acceleration sequences of user-defined length are firstly used to train the implemented ML model and specific damage-sensitive features are afterwards selected to quantify the differences between the original and the reconstructed input. Therefore, unknown testing data can be conceivably classified as damaged when the reconstruction loss notably deviates from the established reference conditions. The proposed approach is validated on the Z24 benchmark bridge by using pseudo-monitoring data stemming from a Finite Element (FE) model, properly calibrated based on the available field information. Specifically, the developed autoencoder is firstly trained with the acceleration responses simulated during operational conditions and collected by different sensors. Then, a realistic earthquake-induced damage scenario is introduced into the FE model in order to generate damaged acceleration time-histories to test the trained ML model. Results prove the capability of the proposed technique to detect seismic-induced damage, making it suitable for a prompt post-earthquake diagnosis.

Hygrothermal aging behavior and mechanism of multi-filler reinforced epoxy composites for steel structure coatings

The long-term hygrothermal resistance of epoxy-based composites plays a key role as an anti-corrosion and anti-wear coating for steel structures when exposed to complex service environments. The hygrothermal aging will cause an irreversible damage to the service performances of composites. In the present paper, the multi-fillers reinforced epoxy composites (MFREC) had been successfully prepared and have been given higher expectations for steel coatings. Its hygrothermal aging behavior was experimentally investigated through the immersion in distilled water (DW) and saline water (SW) at 20, 40, and 60 °C as long as 120 days. The moisture uptake, thermal properties, mechanical properties and microstructure analysis were tested to evaluate the long-term hygrothermal evolution. The research results showed that the quasi-equilibrium moisture uptake content (3.72%) of MFREC in SW was lower than that (4.14%) in DW owing to the preferential permeation and occupation effects of small ions (Na+, Cl−). Hygrothermal exposure led to a maximum degradation of tensile strength, elongation at break and glass transition temperature of MFREC, up to 38.2%, 46.6% and 20.8% in DW, and 34.5%, 40.8% and 18.4% in SW, respectively. This was because the epoxy matrix had generated relaxation and reinforced-fillers/epoxy interfaces had been destroyed, which can be verified by hydrogen bond increasing (maximum 75.2%) from infrared test, fiber pulling out from epoxy matrix (morphology analysis), elastic modulus decreasing (maximum 37.8%) from micro-hardness measurement and pore volume increasing (maximum 53.9%) from N2 adsorption test compared to unaged samples. Based on the Arrhenius theory, the long-term life prediction of MFREC tensile strength under three typical bridge service environments was conducted to evaluate the service time as coatings. It can be found that the stable strength retentions were 63.19% and 66.03% in the simulated pure water and marine environments, respectively. Furthermore, an additional strength degradation percentage up to 11.5% can be found for MFREC exposed to steel bridge coatings in southern as long as 3 years compared to the northern environment.

Comparison between Supervised and Unsupervised Learning for Autonomous Delamination Detection Using Impact Echo

Impact echo (IE) is a non-destructive evaluation method commonly used to detect subsurface delamination in reinforced concrete bridge decks. Existing analysis methods are based on frequency domain which can lead to inaccurate assessments of reinforced concrete bridge decks since they do not consider features of the IE signals in the time domain. The authors propose a new method for IE classification by combining features in the time and the frequency domains. The features used in this study included normalized peak values, energy, power, time of peaks, and signal lengths that were extracted from IE signals after they are preprocessed. We used a dataset containing IE data collected from four in-service bridges, annotated using chain dragging. A support vector machine (SVM) classifier was constructed using combined features to classify IE signals. A 1DCNN with unfiltered IE signals and a two-dimensional CNN using wavelet scalograms (2D representations of unfiltered IE signals) were also used to classify IE signals. The SVM model performed significantly better than the other models, with an accuracy rate, true positive rate, and true negative rate of 97%, 92%, and 98%, respectively. The SVM model also generated more accurate defect maps for all investigated bridges. IE data from the Federal Highway Administration’s InfoBridge website were used to investigate the efficacy of the developed models. The investigation yielded promising results for the proposed SVM model when used for a new set of IE data.

Experimental and numerical study on improving mechanical properties of new polyurethane-emery thin polymer overlay (TPO) structure using three-point bending test

The thin polymer overlay (TPO) is a new type of road surface treatment layer for high traffic pressure and high road performance requirements. Therefore, it is not only widely used on highways but also has more and more applications in the field of bridge deck pavement. In this work, a new type of TPO—the polyurethane-emery TPO composite material was proposed, and a reasonable material mixing ratio is determined. In addition, the flexural and compressive behaviors of the polyurethane-emery TPO reinforced with the steel fiber and cement materials are investigated, and a comparison is made for the development of a numerical model to simulate the behavior of the reinforced-new-TPO using the Finite Element (FE). The experimental tests include three-point bending and compressive tests to evaluate the ultimate capacity of the new-TPO material in flexural and compression and its modulus of elasticity. Moreover, the flexural and compression performance of polyurethane-emery TPO containing different reinforcement configurations was evaluated in three-point bending and compression tests, and the reinforcing effects of different steel fibers and cement contents were investigated. The use of steel fiber and cement-reinforced thin polymer overlay in reinforced bridge surface treatment construction was found to be able to significantly offer several advantages, such as excellent corrosion resistance, and flexural and compression strength. Through experimentation, it can be seen that the new type of polyurethane-emery TPO proposed in this paper can completely replace the existing TPO, however, the effect of the dosage of steel fiber and cement needs to be considered during the design. Meanwhile, a three-dimensional nonlinear finite element model was established by using ABAQUS, which incorporated the second development model of steel fiber based on python and CDP models and can be used to predict the flexural and compression behaviors. The numerical model was experimentally validated by using the three-point bending and compression test results and was used for parametric studies. The parameters investigated in this study were steel fiber content, fiber length, and polyurethane-cement ratio of the polyurethane-emery TPO reinforcement. The proposed new polyurethane-emery TPO system was revealed to be promising for the development of the bridge surface treatment reinforcement formwork and can be used in superstructure construction for the extension of the bridge service life. The numerical and the experimental results were generally in good agreement.

Fatigue reliability analysis of bridge crane metal structure considering maintenance

Periodic inspection and maintenance of cracks have great influence on the reliability and safety of metal structures of bridge cranes. However, the influence of maintenance factors on the fatigue reliability of in-service bridge cranes is rarely considered at present. Taking a metal structure of a certain type bridge crane as an example, this paper establishes the limit state equation according to the residual strength of the structure. Considering the reduction of section modulus by fatigue cracks, the influence of periodic inspection and maintenance is quantitatively analyzed. The fatigue reliability of metal structure of bridge crane is analyzed and calculated by using MATLAB program. The results show that the fatigue damage will lead to the continuous decrease of reliability, and the fatigue reliability of metal structure can be restored to a certain extent by considering maintenance, which delays the decrease of reliability.

Structural Health Monitoring of Large-Scale Bridges: A Synopsis of the Padma Bridge

In the recent decade, the concept of "structural health monitoring," or SHM, has gained prominence due to its promise of reflecting the condition of structures and facilitating the monitoring of their behavior. Bangladesh is a country with a long coastline, thus it is unfortunate that the SHM system has not been more widely deployed on the country's many highway bridges across rivers. Saving money on manpower, remote monitoring allows for accurate, up-to-date assessments of a bridge's structural soundness. Recent developments in sensor, communication, and storage technologies have made a worldwide SHM system for infrastructures possible. The primary goal of this investigation is to assess the performance of the structural health monitoring system on the Padma Multipurpose Bridge. Recent developments in SHM's integration with ITS show the usefulness of ITS devices (such as traffic cameras and traffic detectors) in analyzing bridge responses to multimodal traffic with varying loads or during critical events that cause excessive vibration beyond the normal limit, which can be of great assistance in tackling the Padma bridge's serviceability challenge. Integrating information from an ITS device with SHM may increase the reliability and precision of the SHM system. As a consequence of this integration, the SHM system would be less likely to misdiagnose damages (i.e., vibrations caused by big cars on a bridge may be perceived by a SHM sensor as a structural health concern of the bridge), resulting in decreased maintenance costs. This investigative study provided a summary of the SHM systems now in place for major bridges in Bangladesh, such as the spectacular Padma Bridge, and discussed their use and appropriateness in the near and far future.

Effect Of Creep On The Long-Term Deflection Of Box Girder Balanced Cantilever Bridge Structure Using B3 Model and CEB 2010

Creep significantly affects the long-term deflection of the prestressed concrete bridge structure. Some models often used in predicting creep do not consider the water-cement ratio. The water-cement ratio is a factor in the magnitude of creep. If the water-cement ratio is excessive, the creep will also be significant. B3 Model uses the water-cement ratio in predicting creep in prestressed concrete bridge structures and has provided good accuracy with measured deflection data. This study compares B3 Model with Model CEB 2010 to predict the effect of creep on the long-term deflection. The author modeled the bridge structure using Midas Civil 2022 v1.2 software by utilizing the construction stages analysis facility to idealize the balanced cantilever and the effect of creep on the long-term deflection. Envelope displacement of bridge B3 Model is more significant than CEB 2010. The prediction deflection of the B3 Model in 100 years of service life of the bridge is -16.34 cm, while CEB 2010 is -11.90 cm. Creep affects total deflection by 84% to 88%. Creep affects the deflection significant because, in the construction process, each box girder segment is stressed and loaded at the age of 3 days. At the age of 3 days, the elastic modulus of the concrete is still not entirely, and the cement paste on the concrete is still in the hydration process. The results showed a significant difference between B3 Model and CEB 2010. B3 Model predicts that the long-term deflection of the bridge until the end of the bridge's service life is 44% to 49% greater than the CEB 2010 model. Prediction of total deflection until the end of 100 years of bridge service life does not exceed the limit determined by SNI and CEB codes.

Performance Evaluation of Uncooled UAV Infrared Camera in Detecting Concrete Delamination

Concrete delamination detection using unmanned aerial vehicle (UAV)-mounted infrared cameras has proved effective in recent research. However, most studies used expensive research-grade infrared cameras and proprietary software to acquire images, which is hard to implement in state departments of transportation (DOTs) due to the lack of specialty professionals. Some state DOTs started deploying lightweight UAV-based consumer-grade infrared cameras for delamination detection. Quantitative performance evaluation of such a camera in concrete delamination detection is lacking. To fill this gap, this study intends to conduct a comprehensive assessment of the consumer-grade camera benchmarked against the results of a research-grade camera to see the practicality of using the small and low-cost camera in concrete delamination detection. Data was collected for a slab with mimicked delamination and two in-service bridge decks. For the case of the slab, maximum detectability of 70–72% was achieved. A transient numerical simulation was conducted to provide a supplemental and noise-free dataset to explore detectability accuracy peaks throughout the day. The results of the in-service bridge decks indicated that the consumer-grade infrared camera provided adequate detection of the locations of suspected delamination. Results of both the slab and in-service bridge decks were comparable to those of a research-grade infrared camera.