Heat spots caused by standing waves in an inspected object can pose practical challenges in nondestructive inspection using ultrasonic excitation thermography. This study investigates the dominant wave mode excited during inspection and explores methods to suppress heat generation caused by standing waves. Comparison of experimental results and theoretical calculations showed that the waves propagating in plate objects were dominated by the A₀ mode Lamb waves. As a result, the distribution of the heat spots caused by standing waves varied depending on the frequency of the excitation wave and plate thickness. Based on these findings, we propose a suppression method that eliminates unwanted heat spots by superimposing two Lamb waves with a phase difference that is a natural multiple of half the wavelength.

Typical building inspection using Passive Infrared Thermography involves defect analysis. With different image processing techniques, it is possible to perform defect size estimation. However, the current approach only relies on a single thermal image to estimate the defect size, and the time when the thermal image was captured highly affects the performance. Since defects on the building’s exterior wall are in an ambient environment, the ideal scenario is to collect thermal images of the target with time which captures the whole heat absorption and dissipation period for better defect analysis. But such an approach is unfeasible in urban areas. This study proposes the adoption of the Thermal Decay Gradient Approach in which only two thermal images (captured in the heat absorption period and the heat dissipation period respectively) were used in the defect analysis. A west-facing sample wall with 10 embedded defects in different cover depths was constructed in an outdoor environment, and thermal data of the sample wall were taken once every 5 minutes in a 24-hour cycle. This study considered different combinations of the two thermal images to be processed, and then natural logarithmic and linear regression fitting was applied to the temperature values in the selected thermal images. Then the thermal decay gradient of each pixel was calculated and used to form a visualization in which the defects were highlighted, and the resulting visualization could be used for defect size estimation. This study evaluates the accuracy of the proposed method by comparing the defects’ estimated sizes with actual sizes.

This study investigates in natural environment the thermal behavior of an innovative pavement system with thermal and solar energy collection functionalities. The whole structure is continuously monitored using temperature and heat flux sensors probes integrated inside the structure. The local weather conditions are also monitored. Infrared Thermography is used as a complementary non-invasive technique to monitor temperature surface distributions with time and assess the efficiency of heat transfer within the pavement structure. All sensors are connected to a newly developed platform that centralizes data access, visualization, and storage, enabling seamless management and users interactions. Results obtained are presented and discussed.

Automobiles are required to have both lightweight bodies to improve fuel economy and high rigidity to improve safety. Recently, weld bonding which is a method of joining steel materials is expected to be effective. Weld bonding combines spot welding with adhesive to compensate for the reduced rigidity of the steel plate due to its thinner wall thickness. Since the stiffness-enhancing effect obtained by this technique assumes that the adhesive fills the required area, the bonding area is an important inspection item. Conventional sampling inspection has the problems that only the cut surface can be evaluated and the cut part cannot be used. Therefore, we conducted this study to establish an inspection method that enables nondestructive inspection of all parts by infrared temperature measurement.

In this study, an active infrared thermography method that applies a forced thermal load to the surface to be inspected was used. A test specimen bonded by weld bonding was pulse-heated by a flash lamp, and the temperature fluctuations on the surface of the specimen were captured by an infrared camera. The difference in heat conduction between adhesive-applied and non-applied areas appeared as a temperature difference at the measurement surface, and the bonded area could be detected. Singular value decomposition was performed on the obtained time-series temperature variation data. This method enabled us to extract the temperature fluctuation through the adhesive and to identify the adhesive area more precisely.

Traditional infrared thermography (IRT) techniques can only provide two-dimensional (2D) projections of surface temperatures, and it is difficult to intuitively present the surface profile of the three-dimensional (3D) structure and the spatial distribution of the internal defects. In this paper, a low cost, high efficiency and high precision photothermal 3D tomography technology was proposed by combing 3D thermography and infrared tomography for the first time. Specifically, this paper abandoned additional 3D sensors and complex motion systems such as robotic arms and scanning platforms. Differently, it utilized a galvanometer to deflect laser for line structure light scanning, and captured the thermal stripes generated on the structural surface using an infrared camera, and then realized the highly efficient 3D reconstruction; moreover, this work introduced undersampling strategy into photothermal coherence tomography (PCT) technology to enhance the ranging depth, and the ranging results were transmitted to the 3D coordinate system, so as to realize the photothermal 3D tomography. Through the detection experiments of metal additive manufacturing parts, it was shown that the proposed method could reconstruct the 3D contour of the specimen, and identified the first heterogeneous interface below the surface of the specimen.

This paper reports that applicability of fatigue crack detection with infrared thermography camera, T-gap method, to the steel bridge in Denmark. T-gap method is a non-destructive test developed in Japan and does not need to approach closely to the bridge members rather than visual inspections. The principle of T-gap method is to measure the thermal profile of welding point. One of the crucial factors generating temperature gap is the solar altitude, so that there is less solar altitude in high latitude area and it is unclear that whether T-gap method is applicable in higher latitude area than Japan. Then, trial of T-gap method in Denmark, which is located at higher latitude than Japan, was planned to grasp its applicability. As the results of trials, T-gap method successfully detected both locations and length of cracks even in Denmark.

The application of sub-terahertz waves is being considered as a method to view the cor­rosion status of internal steel bars. In this study, reflectance intensities were measured for six specimens with different moisture contents using electromagnetic waves from 7.5 to 17.5 GHz. The results showed that the differential reflectance intesity decreased with increasing water con­tent. An equation for estimating the water content at a cover thickness of 10 mm was also calcu­lated. Regarding chloride ion content, no common trend was observed, and it was confirmed that there was a trend in the amount of chloride ions at each cover thickness.

In nondestructive evaluation of concrete structures, the far-infrared region, including terahertz waves, which can penetrate concrete and measure the amount of corrosion in the internal steel, has attracted much attention. Magnetite has the potential to be used as a far-infrared detection device that meets the requirements for nondestructive evaluation devices, such as room temperature operation and portability, while also having a low environmental impact. In this study, the sensitivity of magnetite thin films with different concentrations of Pt to electromagnetic waves at a wavelength of 10.6 ㎛ was evaluated and compared: nanocomposite with Pt nanocrystals despersed in magnetite thin films were prepared by radio-frequency sputtering, electrodes were prepared by a photoresist process, and the resistance variation was recorded after irradiation with 10.6 ㎛ pulse electromagnetic waves. As a result, it was experimentally confirmed that the peak of response was the maximum at the amount of Pt added where the electrical resistivity reached 12,000 µΩcm, and the S/N ratio was the maximum at the amount of Pt added where the electrical resistivity reached 14,000 µΩcm. This indicates that Pt-doped magnetite with a Pt content of 14,000 µΩcm electrical resistivity is suitable as a far-infrared detector element material.

The automatic detection and accurate geolocation of key railway objects plays a crucial role in the mapping, monitoring and management of railway infrastructure. This study presents a novel approach for the identification and geolocation of key railway elements through point cloud analysis. The methodology relies on high-density LiDAR point clouds acquired along railway lines using a Mobile Laser Scanning system operating in the Infrared (IR). This research contributes to the advancement of railway mapping and monitoring technologies by providing an innovative solution that can be integrated into railway infrastructure management software.

Using a camera system developed earlier for monitoring the behavior of lemmings under the snow, we are now able to record a large number of short image sequences from this rodent which plays a central role in the Arctic food web. Identifying lemming species in these images manually is wearisome and time-consuming. To perform this task, we present a deep neural network which has several million parameters to configure. Training a network of such an immense size with conventional methods requires a huge amount of data but a sufficiently large labeled dataset of lemming images is currently lacking. Another challenge is that images are obtained in darkness in the near infrared spectrum, causing the loss of some image texture information. We investigate whether these challenges can be tackled by a transfer learning approach in which a network is pretrained on a dataset of visible spectrum images that does not include lemmings. We believe this work provides a basis for moving toward developing intelligent software programs that can facilitate the analysis of videos by biologists.