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Frequent road inspections are key to maintaining road quality and avoiding casualties associated with poor road conditions. In Taiwan, open contractors conduct inspections of roads and ancillary facilities daily or weekly according to the requirements of the agency awarding the contracts. Unfortunately, the equipment used for inspections the inspection data lacks follow-up applications and numerical conversions, such as the Pavement Condition Index (PCI), to compile a large-scale database to facilitate the long-term conservation of roads. In this study, this paper developed back-end image recognition software using existing road inspection methods and existing equipment. This was aimed at enhancing inspection efficiency by enabling the automatic identification of road damage. Resulting observations can then be converted into PCI values in accordance with ASTM D6433-16 to be exported as a numeric value indicative of road quality. A vehicle-mounted traffic recorder and imaging device with Wi-Fi transmission capability are used as hardware, and the relationship between the captured images and the speed of the car is used to obtain an accurate indication of road conditions across the surface. The simple linear iterative clustering (SLIC) Superpixels algorithm is used to identify areas with pavement damage as patches, potholes, longitudinal cracking, and crocodile cracking. The results of the proposed fully-automated method conform strongly with those obtained using semi-automated pavement inspection software. Despite the restrictions imposed by the limited depth measurement of 2D images, our method achieved results close to those obtained using manual inspection. Future developments will include the application of artificial intelligence to enhance the effectiveness of this software. [ABSTRACT FROM AUTHOR]

Most of the pipelines in Taiwan lie beneath road pavements. Maintaining existing pipelines and establishing new pipelines inevitably leads to the wide-scale excavation and backfilling, which can greatly interfere with traffic patterns. Controlled low-strength material (CLSM) is commonly used as backfill material because it is highly fluid, self-filling, ideally suited to re-excavation, and does not require ramming. According to American Concrete Institute (ACI) specification, CLSM requires load-bearing capacity sufficient to avoid surface settlement and/or rutting. The water-to-cement (W/C) ratio, water-to-solid (W/S) ratio, curing time and temperature were all shown to affect compressive strength. Besides, the temporarily restore surface have to pave quickly due to the heavily traffic volume in situ. Therefore, the road response is strongly related to the initial compressive strength of CLSM after backfilling. Rushing the setting time can compromise strength; however, extending the setting time can adversely affect traffic flow. To understand the parameters effect, we consider different W/C, W/S, curing time and temperature of CLSM specimens in this study. The soil extracted through excavation was used as the aggregate for CLSM and conducted compressive strength experiments on CLSM. By the experimental data, we build a regression model considered the parameters effect and predicted the compressive strength of CLSM precisely. Furthermore, we used Linear Variable Differential Transducers (LVDT) and program to plot the stress-strain curves; it can get Young's Modulus from the curves and build the relationship of Young's Modulus and compressive strength of CLSM. These models can be used in the finite element analysis and analyze the pavement response after the backfilling. [ABSTRACT FROM AUTHOR]