EVALUASI KERUSAKAN PERKERASAN LENTUR DENGAN METODE PCI DAN SDI (STUDI KASUS: JALAN JATISARI, KARAWANG)

Large vehicles that repeatedly pass a road cause damage to the pavement of the Jatisari National Road, Karawang. Various pavement damage that occurs such as holes, patches, crocodile skin cracks, groove cracks, sungkur, roadside cracks, and subsidence. Pavement Condition Index (PCI) is a method commonly used to indicate the condition of road pavement, so that it can be known good handling to maintain the pavement. The Surface Distress Index (SDI) method can also be used to indicate the condition of the road surface. With the PCI method, the results of the calculation in the Pamanukan direction are classified as perfect at 78%, very good 14%, good 4% and moderate 4%. while the Cikampek direction is classified as perfect at 74%, very good 12%, good 8%, moderate 4%, and bad 2%. Using the SDI method, good results were obtained for both directions. Based on the results of the analysis, research using the PCI and SDI methods showed different results, because the PCI method observed all the damage that occurred on the pavement, while the SDI method only observed 4 elements of damage, so the results displayed were different. ABSTRAKKendaraan besar yang berulang kali melewati sebuah jalan menyebabkan kerusakan pada perkerasan Jalan Nasional Jatisari, Karawang. Berbagai Kerusakan perkerasan yang terjadi seperti, lubang, tambal, retak kulit buaya, retak alur, sungkur, retak tepi jalan, dan amblas. Pavement Condition Index (PCI) merupakan metode yang biasa digunakan untuk menunjukkan kondisi perkerasan jalan, sehingga bisa diketahui penanganan yang baik untuk memelihara perkerasan jalan tersebut. Selain itu, digunakan metode Surface Distress Index (SDI) untuk menunjukkan kondisi permukaan jalan. Dengan Metode PCI, hasil perhitungan pada arah Pamanukan digolongkan sempurna sebesar 78%, sangat baik 14%, baik 4% dan sedang 4%. sedangkan pada arah Cikampek digolongkan sempurna sebesar 74%, sangat baik 12%, baik 8%, sedang 4%, dan buruk 2%. Dengan metode SDI, diperoleh hasil Baik untuk kedua arah jalan. Berdasarkan hasil analisis, penelitian menggunakan metode PCI dan SDI menunjukkan hasil yang berbeda, dikarenakan dalam metode PCI mengamati semua kerusakan yang terjadi pada perkerasan jalan, sedangkan untuk metode SDI hanya mengamati 4 unsur kerusakan, sehingga hasil yang ditampilkan berbeda.

Bearing Skidding Detection for High Speed and Aero Engine Applications

Rolling-element bearings (REB) can develop severe damage due to skidding (slipping) between the rolling elements and bearing races. Skidding can be described as gross sliding between the bearing surfaces in relative motion and can result in significant surface distress such as smearing, especially at light loads and high rotational speeds. Under these conditions, skidding occurs between the rolling elements and the bearing races, leading to increased wear (higher friction coefficient), elevated bearing temperature, significant power losses and reduced service life of the bearing. The main objective of this study is to investigate the significance of various sensing technologies (induction, vibration, ultrasound, acoustic and optical) in detecting skidding in standard series roller bearings as well as custom-made roller bearings for aero engine applications. The bearings have a bore diameter of 60 mm and 90 mm, respectively. Jet and under race lubrication techniques have been used to supply oil to the bearings under test. The custom-made aero engine test bearing features special channels to allow under race lubrication of the rollers/races contacts as well as the cage land. The effect of radial load, rotational speed and oil flow on roller skidding have also been investigated and analyzed. Tests have been performed on a dedicated high speed experimental bearing facility and data was recorded using a commercially-available data acquisition system.

Assessment of Relationship between Road Roughness and Pavement Surface Condition

Pavement evaluation is the most significant procedure to minimize the degradation of the pavement both functionally and structurally. Proper evaluation of pavement is hence required to prolong the life year of the pavement, which thus needs to be addressed in the policy level. By this, the development of genuine indices are to be formulated and used for the evaluation. In context of evaluating the pavement indices for measuring the pavement roughness, International Roughness Index (IRI) is used, whereas for calculating the surface distress, indices as such Surface Distress Index (SDI) and Pavement Condition Index (PCI) are used. Past evaluating schemes used by Department of Roads (DOR) were limited to IRI for evaluating the pavement roughness and SDI for measuring the surface distress, which has least variability in categorizing the pavement according to the deformation. Apart from these, PCI which has wide range of categories for evaluating pavement, is not seen in practice in Nepal due to its cumbersome field work and calculations. In this paper the relationship is developed relating PCI with IRI and SDI using regression analysis by using Microsoft excel. In the other words, the pavement roughness index is compared with the surface distress indices. In 2017, 23.6Km of feeder roads in various locations of Kathmandu and Lalitpur districts were taken for this study which comprised of 236 sample data, each segmented to 100m. For this, IRI was sourced as secondary data, obtained from Highway Maintenance and Information System (HMIS) unit, Kathmandu, whereas, PCI and SDI were calculated from the field data obtained from the survey carried out in those sections manually. Then after, among 236 samples, 189 samples were taken for the relationship development which was then validated using 47 remaining samples. Furthermore, in the year, 2019 additional 3 Km of data was taken for validating the obtained relationships. It was done to improve the numerical predictions of data with such variation and thus satisfactory relationships were developed among the indices discussed in this study. The regression relationships between the two indices, IRI-PCI and IRI-SDI were thus significantly obtained. It has been found that the R² value for these relationships developed were statistically significant with 5% level of significance. The R² value for all the relationships showed that these relationships could be used for predicting the indices which would help in evaluating the pavement.

Long-Term Performance of Pavement Structures with Cold In-Place Recycled Base Course

Properly designed and maintained asphalt pavements operate for ten to twenty-five years and have to be rehabilitated after that period. Cold in-place recycling has priority over all other rehabilitation methods since it is done without preheating and transportation of reclaimed asphalt pavement. Multiple researches on the performance of cold recycled mixtures have been done; however, it is unclear how the entire pavement structure (cold recycled asphalt pavement overlaid with asphalt mixture) performs depending on binding agents. The main objective of this research was to evaluate the performance of cold in-place recycled asphalt pavements considering binding agents (foamed bitumen in combination with cement or only cement) and figure out which binder leads to the best pavement performance. Three road sections rehabilitated in 2000, 2003, and 2005 were analysed. The performance of the entire pavement structure was evaluated in terms of the International Roughness Index, rut depth, and pavement surface distress in 2013 and 2017.

Bearing Skidding Detection for High Speed and Aero Engine Applications

Rolling-element bearings (REB) can develop severe damage due to skidding (slipping) between the rolling elements and bearing races. Skidding can be described as gross sliding between the bearing surfaces in relative motion and can result in significant surface distress such as smearing, especially at light loads and high rotational speeds. Under these conditions, skidding occurs between the rolling elements and the bearing races, leading to increased wear (higher friction coefficient), elevated bearing temperature, significant power losses and reduced service life of the bearing. The main objective of this study is to investigate the significance of various sensing technologies (induction, vibration, ultrasound, acoustic and optical) in detecting skidding in standard series roller bearings as well as custom-made roller bearings for aero engine applications. The bearings have a bore diameter of 60 mm and 90 mm, respectively. Jet and under race lubrication techniques have been used to supply oil to the bearings under test. The custom-made aero engine test bearing features special channels to allow under race lubrication of the rollers/races contacts as well as the cage land. The effect of radial load, rotational speed and oil flow on roller skidding have also been investigated and analyzed. Tests have been performed on a dedicated high speed experimental bearing facility and data was recorded using a commercially-available data acquisition system.

Deep Learning to Detect Road Distress from Unmanned Aerial System Imagery

Surface distress is an indication of poor or unfavorable pavement performance or signs of impending failure that can be classified into a fracture, distortion, or disintegration. To mitigate the risk of failing roadways, effective methods to detect road distress are needed. Recent studies associated with the detection of road distress using object detection algorithms are encouraging. Although current methodologies are favorable, some of them seem to be inefficient, time-consuming, and costly. For these reasons, the present study presents a methodology based on the mask regions with convolutional neural network model, which is coupled with the new object detection framework Detectron2 to train the model that utilizes roadway imagery acquired from an unmanned aerial system (UAS). For a comprehensive understanding of the performance of the proposed model, different settings are tested in the study. First, the deep learning models are trained based on both high- and low-resolution datasets. Second, three different backbone models are explored. Finally, a set of threshold values are tested. The corresponding experimental results suggest that the proposed methodology and UAS imagery can be used as efficient tools to detect road distress with an average precision score up to 95%.

DETERMINING ROAD HANDLING ACCORDING TO THE LEVEL OF DAMAGE USING SURFACE DISTRESS INDEX (SDI) METHOD

<p>Roads that have been functioned are in good condition, slightly damaged, moderately damaged, and heavily damaged, therefore road maintenance is needed. Road maintenance uses costs, and the available costs are often insufficient to carry out road repairs as a whole, so it is necessary to determine the priority scale of road repairs. The Surface Distress Index (SDI) method is a method used by the DGH to determine the level of road damage, furthermore as a basis for determining the priority scale for road repairs. Along 2.25 km of Jalan Merdeka 1, Sukabumi City, it is the sampling location for the study to determine the condition of road damage. Each investigation point is determined to be 200 m long, starting from the initial STA 0 + 000 - 0 + 200 to the last STA 2 + 200 - 2 + 250. The results showed that the road conditions consisted of moderately damaged, lightly damaged to heavily damaged, so it needed maintenance at STA 0 + 000 - 0 + 400, it needed rehabilitation at STA 0 + 400 - 1 + 800 and STA 2 + 200 - 2 + 250 , as well as need reconstruction at STA 1 + 800 - STA 2 + 200. The results of this study can be used as a basis for determining road handling by policymakers.</p>