Roughness Index Research Articles

Reverse calculation and quantitative estimation of JRC3D for rough rock fracture surfaces in various shear directions

The quantitative evaluation of the three-dimensional roughness feature of natural rock fracture surface is essential for predicting shear strength. This article presents an effective approach to estimate the three-dimensional roughness of natural fracture surfaces from the two-dimensional roughness of fracture profile. Firstly, four types of rough fracture samples were created using rock-like materials, followed by conducting direct shear tests of the fracture surfaces under 4 different normal pressures and 4 shear directions. The results of the shear strength tests exhibited a distinct discrete pattern and consistently increased with higher normal pressures. Furthermore, the distribution of the damage area on the fracture surface after shear was significantly impacted by the distribution and size of asperities, as well as the shear direction. The three-dimensional roughness of four fracture surfaces in different directions was first reverse calculated using the shear strength model. Using the weighted positive angle, the estimation of the three-dimensional roughness in different directions of the fracture surface was then determined from the two-dimensional roughness of fracture profiles using both the arithmetic mean method and the weighted average method. The weighted average method effectively captures the contribution of the two-dimensional roughness of fracture profiles to the three-dimensional roughness of fracture surfaces, and exhibits a lower estimation error compared to the arithmetic mean method. Compared to other roughness indicators, the three-dimensional roughness evaluated through the weighted positive angle indicator are closest to the experimental result. Finally, the influence of transverse spacing on the estimated three-dimensional roughness was discussed.

Characterization of the microstructure evolution features of various aggregates based on confocal laser scanning microscope

To reveal the microscale structural evolution characteristics of aggregates, this study employs confocal laser scanning microscope (CLSM) to conduct the surface microstructures of six aggregates: limestone, basalt, granite, diabase, sandstone, and recycled aggregate (RA). Quantitative analysis was performed on the surface morphology, roughness parameters, and material ratio of the aggregates. The results indicate that the surface features of the aggregates vary with the type and treatment method, and these distinctions may be attributed to variations in diagenetic conditions. In the original state, the surface texture of limestone, basalt, and diabase is relatively minimal, with arithmetic mean height (Ra) distributed between 1.4 μm and 3.5 μm. Conversely, the surface texture of granite, sandstone, and RA is more complex, with Ra distributed between 7.5 μm and 9.5 μm. The roughness indices of basalt, limestone, and diabase show an increasing trend after lime treatment, indicating a more pronounced presence of microscopic peaks and pits. In contrast, all roughness indices of granite, sandstone, and RA exhibit a declining after lime treatment. After polishing, the Ra of the surfaces of the six different aggregates shows varying degrees of reduction. Specifically, Ra decreased by 43.89%, 33.11%, 26.52%, 30.14%, 29.80%, and 38.79% for limestone, basalt, granite, diabase, sandstone, and RA, respectively. Furthermore, the height distribution frequency and material ratio of aggregate surfaces vary with the type and treatment of aggregates. Through grey correlation analysis, the correlation degrees of root mean square slope (Rdq), kurtosis (Rku), and surface texture aspect ratio (Rtr) are 0.9524, 0.9522, and 0.9514, respectively, belonging to hybrid parameter, height parameter, and spatial parameter. This suggests a close relationship between these parameters and the polished stone value (PSV) of the aggregates. These findings offer valuable insights for improving aggregate selection and treatment processes, potentially leading to enhanced durability and performance of construction materials.

Novel Instance-Based Transfer Learning for Asphalt Pavement Performance Prediction

The deep learning method has been widely used in the engineering field. The availability of the training dataset is one of the most important limitations of the deep learning method. Accurate prediction of pavement performance plays a vital role in road preventive maintenance (PM) and decision-making. Pavement performance prediction based on deep learning has been widely used around the world for its accuracy, robustness, and automation. However, most of the countries in the world have not built their pavement performance historical database, which prevents preventive maintenance using the deep learning method. This study presents an innovative particle swarm optimization (PSO) algorithm-enhanced two-stage TrAdaBoost.R2 transfer learning algorithm, which could significantly increase the pavement performance prediction database. The Long-Term Pavement Performance (LTPP) database is used as the source domain data, and one of the highways in China is chosen as the target domain to predict pavement performance. The results show that the proposed PSO-Two-stage TrAdaBoost.R2 model has the highest accuracy compared with AdaBoost.R2 model and traditional regression decision tree model. The validation case study shows significant consistency between the predicted International Roughness Index (IRI) and the whole-year measurement data with an R2 of 0.7. This study demonstrates the great potential of the innovative instance-based transfer learning method in pavement performance prediction of a region’s lack of data. This study also contributes to other engineering fields that could greatly increase the universality of deep learning.

Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-Pixel Accuracy: Application to Digital Elevation Models

In recent decades, an important number of regional and global digital elevation models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs need to be expressed in the same coordinate reference system (CRS) and sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention) with heights relative to the same vertical reference system (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements or disparities may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect, or curvature. Therefore, before any comparison, the control of DEM planimetric accuracy is certainly a very important task to perform. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have already been computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been introduced through bicubic interpolation in an 11 × 11 grid with a 0.1-pixel step in both directionsThis validation method has been applied in four approximately 10 km × 10 km DEMIX tiles showing different roughness (height distribution). Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with typical errors (eb) ranging from 12 to 20% of the pixel size (with the worst case in Croatia). These errors in displacement retrievals are not equally distributed in the 11 × 11 grid, and the overall error Eb depends on the roughness encountered in the different tiles. The second aim of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d = 1 of the interpolated point) on the accuracy of the displacement retrieval. By considering Eb as a quality indicator, tests have been performed in the four DEMIX tiles, making the bicubic parameter vary between −1.5 and 0.0 by a step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four Eb minimal values. This BBC parameter b* is low for flat areas (around −0.95) and higher in mountainous areas (around −0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r = 0.717). The logarithmic regression formula b~σslope estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC b~.

Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking

Roads degrade over time due to various factors such as traffic loads, environmental conditions, and the quality of materials used. Significant investments have been poured into road construction globally, necessitating regular evaluations and the implementation of maintenance and rehabilitation (M&R) strategies to keep the infrastructure performing at a satisfactory level. The development and refinement of performance prediction models are essential for forecasting the condition of pavements, especially to address longitudinal cracking distress, a major issue in thick asphalt pavements. This research leverages multiple machine learning methods to create models predicting non-wheel path (NWP) and wheel path (WP) longitudinal cracking using data from the Long-Term Pavement Performance (LTPP) program. This study highlights the marked differences in distress conditions between WP and NWP, underscoring the importance of precise models that cater to their unique features. Aging trends for both types of cracking were identified through correlation analysis, showing an increase in WP cracking with age and a higher initial International Roughness Index (IRI) linked to NWP cracking. Factors such as material characteristics, kinematic viscosity, pavement thickness, air voids, particle size distribution, temperature, KESAL, and asphalt properties were found to significantly influence both WP and NWP cracking. The Exponential Gaussian Process Regression (GPR) emerged as the best model for NWP cracking, showcasing exceptional accuracy with the lowest RMSE of 89.11, MSE of 7940.72, and an impressive R-Squared of 0.63. For WP cracking, the Squared Exponential GPR model was most effective, with the lowest RMSE of 12.00, MSE of 143.93, and a high R-Squared of 0.62. The GPR models, with specific kernels for each cracking type, proved their adaptability and efficiency in various pavement scenarios. A comparative analysis highlighted the superiority of our new machine learning model, which achieved an R2 of 0.767, outperforming previous empirical models, demonstrating the strength and precision of our machine learning approach in predicting longitudinal cracking.

High nonlinear optical efficiency in zinc(II) and copper(I) stilbene based iminopyridine complexes: Absorption, emission analysis and advanced characterization

The novelty of this work lies in the comprehensive studies of the spectroscopic and third-order nonlinear optical properties of zinc(II) and copper(I) stilbene based iminopyridine complexes. Our previous work involved the synthesis as well as the characterization of both the free ligand and these its corresponding metal complexes and conducting quantum chemical calculations on their molecular orbitals, shedding light on their charge transfer and π-π* interactions. This makes these materials promising models for supramolecular assemblies. Thin films, obtained using the spin coating method, are characterized using Atomic Force Microscopy and Ellipsometry methods. These techniques provide valuable information about the physical properties of the layers, such as homogeneity, roughness and linear refractive index. Moreover, we present spectroscopic analysis that involve the absorption and emission of light, along with the decay time of photoluminescence in the thin films. Through advanced techniques like the Maker fringe method for third harmonic generation and the Z-scan method for recording nonlinear absorption and refraction, we demonstrate the high nonlinear optical efficiency of these complexes. This study presents how specific structural elements impacts tailoring in the enhancement of the NLO properties of these complexes. These findings underscore the potential of zinc(II) and copper(I) stilbene based iminopyridine complexes in nonlinear optics and photonics applications.

COMPARATIVE ANALYSIS OF EVALUATION CRITERIA FOR UNPAVED RURAL ROADS: A CASE STUDY

This study focused on the evaluation of the condition of the unpaved rural road: Sauces Norte-Imbana in the province of Loja, using two main indicators: Unsurfaced Road Condition Index (URCI) and the International Roughness Index (IRI). The IRI was measured using three different devices: MERLIN, Roughometer III, and a mobile application. The collected data were statistically analyzed to determine their distribution and the correlation between the variables. The results revealed a weak correlation between these variables, indicating that each methodology may be assessing different aspects of the road condition or due to the particular conditions of the road. The lack of a strong relationship may be related to the specific characteristics of the studied road, such as its sinuosity and the influence of environmental conditions on roughness measurements. This study underscores the importance of considering a variety of factors when evaluating the condition of unpaved roads and the need to develop and apply suitable evaluation techniques for the specific conditions of each road.

Ensemble CART surrogate-assisted automatic multi-objective rough fuzzy clustering algorithm for unsupervised image segmentation

Multi-objective clustering algorithms (MOCAs) are popular in unsupervised image segmentation due to their merit of meeting multiple segmentation requirements and the prospect of automatically estimating the number of clusters. However, most of them suffer from high time costs and are easily to be influenced by the uncertainty when handling real complex images. To address these issues, we propose an ensemble classification and regression tree (CART) surrogate-assisted automatic multi-objective rough fuzzy clustering (ECS-AMRFC) algorithm for unsupervised image segmentation. Firstly, a cluster medoid-based encoding scheme is employed to represent solutions with different number of clusters and meanwhile lessen the length of encoding. Then, we design an ensemble CART as the surrogate model to significantly reduce the computational burden. Moreover, a surrogate model management strategy is proposed to accelerate the optimization and enhance the quality of surrogate modeling. To handle the uncertainty in data, we extend the rough fuzzy clustering into MOCAs and construct three complementary objective functions to seek proper cluster medoids from multiple perspectives. In addition, the Gaussian kernel is introduced into the objective functions to handle image pixels that cannot separate linearly in the feature space. Finally, a kernelized rough fuzzy clustering validity index is defined to automatically select the optimal solution with no requirements of any prior knowledge. Experiments show that ECS-AMRFC not only identifies appropriate number of clusters on different kinds of images, but also obtains better segmentation results than state-of-the-art rough fuzzy clustering algorithms and automatic MOCAs.

Estimating impact of pavement surface condition and geometrics design on two-wheeler run-off road crashes on horizontal curves

Introduction-Motorized Two-Wheeler (MTW) is a convenient and affordable mode of transportation despite being highly vulnerable to rollover crashes. The possible fact may be that MTW is a single-track vehicle and thus lack longitudinal stability, particularly on horizontal curves (HC). This stability depends on external factors such as curve geometrics and contact surface properties. Therefore, this research aims to identify the impact of pavement surface conditions and roadway geometrics on MTW run-off road crashes on HC. Method-To consider the temporal effect of pavement surface conditions, this study analyzed different combinations of light and surface moisture conditions for pre- and post-pavement surfacing conditions. Thus, Generalized Estimating Equation (GEE) linked with the log function was used to account for repeated observations. Sample data were collected bi-annually from 76 HC for two years of pre- and post-resurfacing. Pavement surface condition parameter includes skid resistance, rut depth, and international roughness index (IRI). However, to restrain the impact of pavement surface conditions, a separate analysis was carried out to investigate the impact of geometrical design attributes. Results-The analysis concluded that surface moisture condition, light condition, rut depth, and their interactions with AADT were found significant for pre-resurfacing. For post-resurfacing, surface moisture condition, and light condition, IRI and its interaction with AADT were found significant. Similarly, roadway geometrics such as curve radius and their interaction with the presence of sight distance, public activity centre and cross-slope were found significant. Conclusion-Overall, the study insights and confirms how and to what extent pavement surface properties and geometric attributes impact the subject crashes. The results will enhance the understanding of roadway architecture and may help highway designers to implement, either at the intervention or treatment level, to improve safety and comfort.

Quantifying the Effects of Material Input Levels on Jointed Plain Concrete Pavement (JPCP) Performance and Slab Thickness

The mechanistic-empirical pavement design guide (MEPDG) is a commonly accepted design principles guide that aids in jointed plain concrete pavement (JPCP) design and performance analysis. The MEPDG uses three different design parameter input levels, referred to as level one, level two, and level three, providing increasing confidence in the analysis at the lower numbered levels, which use more locally relevant (level two) or project-specific (level one) data. The state-of-the-art pavement ME software (version 2.6.2) uses MEPDG design principles to predict pavement performance. The three performance indicators for JPCP systems (international roughness index (IRI), joint faulting, and transverse cracking) experience significant changes when simulating under a different input level. The IRI and faulting indicator changed by 78 percent when using inputs varying from level one to level three, with the cracking indicator change being more severe at 87 percent. To accommodate the change in performance indicator values between input level one and input level three, increasing the concrete slab thickness is necessary to achieve comparable pavement performance. An increase in the Portland cement concrete (PCC) layer from one inch to two inches is required when input level three simulations are performed, demonstrating the economic and sustainability benefits of using project-specific level one inputs. Understanding the impact of simulation input levels will help to meet design and sustainability goals and improve the lifecycle performance of JPCP systems.

Physics-informed neural networks to advance pavement engineering and management

Physics-informed neural networks (PINN) are machine learning (ML) algorithms that can bridge the gap between our understanding of physical phenomena and the corresponding empirical observations. This paper discusses applications of physics-integrated ML to advance pavement engineering. To demonstrate an example, a PINN model was pretrained to approximate the simulation of vehicles’ suspension responses to longitudinal road profiles. The parameters of the outer layers were finetuned to adapt model output to the standard International Roughness Index (IRI), while keeping the pretrained inner layers to preserve the embedded physical knowledge of the suspension behaviour. The PINN model showed low bias and standard error in predicting IRI values on training, test, and an independent dataset from an autonomous vehicle study by the Ford Motor Company. This approach to reconcile and supplement infrequent survey data with spatiotemporally continuous data (from connected vehicles) can enhance data-driven practices for pavement design, maintenance and asset management.

Applications of using connected vehicle data for pavement quality analysis

Current quantitative methods to evaluate pavement conditions in the United States are most commonly focused on construction acceptance using the International Roughness Index (IRI). However, from an asset management perspective, qualitative visual inspection techniques are the most prevalent. Modern vehicles with factory-equipped sensors drive these roadways daily and can passively assess the condition of infrastructure at an accuracy level somewhere between qualitative assessment and rigorous construction acceptance techniques. This paper compares crowdsourced ride quality data with an industry standard inertial profiler on a 7-mile bi-directional construction zone. A linear correlation was performed on 14 miles of I-65 that resulted in an R2 of 0.7 and a p-value of <0.001, but with a modest fixed offset bias. The scalability of these techniques is illustrated with graphics characterizing IRI values obtained from 730,000 crowdsourced data segments over 5,800 miles of I-80 in April of 2022 and October 2022. This paper looks at the use of standard original equipment manufacturer (OEM) on-board sensor data from production vehicles to assess approximately 100 miles of roadway pavements before, during, and after construction. The completed construction projects observed IRI improvements of 10 in/mi to 100 in/mi. These results suggest that it is now possible to monitor pavement ride quality at a system level, even with a small proportion of connected vehicles (CV) providing roughness data.

Modeling of road damage prediction due to overload using HDM-4 method

The road use load should essentially conform to the plan load as stated in the design guidelines. But in reality, the load received by the road is more than standard, so it has the potential to accelerate road damage. This study aims to predict the increase in the value of the International Roughness Index (IRI) and model the increase in value (IRI) due to overload. The approach method used in the analysis is the prediction of an increase in the IRI value due to excess load. The data collection method is carried out on road sections which include IRI values, vehicle load testing, CBR values and environmental conditions. The location of data collection is carried out on BTS Provincial Road. Surabaya City – Legundi. The analysis method for the prediction of the IRI value is the HDM-4 method, while to model the prediction of the increase in the IRI value is used exponential method with various parameters related to the results. The prediction results show that overload affects the increase in the IRI value. From the initial IRI value of 2.37, if the road is traversed by vehicles with standard loads for 20 years, the IRI value increases to 10.42. While the road traveled by overloaded vehicles within 20 years from an IRI value of 2.37 will increase to 16.88. With overload, the IRI value increases very sharply with the exponential model y=2.3583e0.0982x. While the standard load by generating an exponential model y=2.3825e0.0739x. From the results of the exponential model, load overload resulted in a significant increase in the IRI value with a growth rate of 0.0982. When compared to normal expenses which only experienced a growth rate of 0.0739. The modeling results show that overload will increase the growth of very high IRI values with the exponential model increase graph

Development of a Relationship between Pavement Condition Index and Riding Quality Index on Rural Roads: A Case Study in China

The current standard for evaluating road conditions worldwide relies primarily on the Pavement Condition Index (PCI) and the International Roughness Index (IRI). The IRI can be further calculated to obtain the Riding Quality Index (RQI). To assess pavement damage, various imaging equipment is commonly utilized, providing consistent results that align with actual road conditions. For roughness detection, the Laser Profilometer offers excellent results but may not be suitable for rural roads with poor conditions due to its high inspection cost and the need for a stable environmental setting. Therefore, there is a pressing need to develop cost-effective, rapid, and accurate roughness inspection methods for these roads, which constitute a significant portion of the road network. This study examined the relationship between PCI and RQI using nonlinear regression on 30,088 valid pavement inspection records from various regions in China (totaling 24,624.222 km). Our objective was to estimate RQI solely from PCI data, capitalizing on its broad coverage and superior accuracy. Additionally, we explored how PCI levels impact RQI decay rates. The models in this study were compared to several models published in previous studies at last. Our findings indicate that the model performs best for low-grade roads with low PCI scores, achieving over 90% accuracy for both cement concrete and asphalt concrete pavements. Furthermore, different levels of pavement damage have distinct effects on RQI decay rates, with the most significant impact observed when the pavement is severely damaged. The models in this study outperformed all the other available models in the literature. Consequently, under limited inspection conditions in rural areas, pavement damage inspection results can effectively predict riding quality or roughness, thereby reducing inspection costs. Overall, this study offers valuable insights but has limitations, including limited global generalizability and the model’s applicability to high-grade roads. Future research is needed to address these issues and enhance practical applications.

Research on a multi-dimensional image information fusion algorithm based on NSCT transform

Traditional inspection cameras determine targets and detect defects by capturing images of their light intensity, but in complex environments, the accuracy of inspection may decrease. Information based on polarization of light can characterize various features of a material, such as the roughness, texture, and refractive index, thus improving classification and recognition of targets. This paper uses a method based on noise template threshold matching to denoise and preprocess polarized images. It also reports on design of an image fusion algorithm, based on NSCT transform, to fuse light intensity images and polarized images. The results show that the fused image improves both subjective and objective evaluation indicators, relative to the source image, and can better preserve edge information and help to improve the accuracy of target recognition. This study provides a reference for the comprehensive application of multi-dimensional optical information in power inspection.Graphical

Triad’s consonance and dissonance: combining roughness and compactness models

Theories of consonance and dissonance based on the “roughness” approach are those that explain these perceptions as due to the primary beatings between harmonics. Originally proposed by Helmholtz, this approach has been very popular in the last century, being naturally associated to continuous functions of the frequency ratios, on the contrary of theories based on the “compactness” approach. In a previous work, we focused on the roughness consonance and dissonance indicators for dyads, showing the importance of including weight functions and especially secondary beatings. Here, we generalize the roughness indicators to describe the consonance and dissonance for triads. We compare our model predictions with perceptual data from a recent psychoacoustic test by means of a Chi-square analysis. The result is that roughness indicators provide a quite effective, but not fully satisfactory, description of consonance and dissonance for triads. We then study the effect of combining roughness and compactness models for triads: in this case, a very satisfactory agreement with perceptual data is achieved.

Cycling on rough roads: a model for resistance and vibration

Minimising opposing forces is a matter of interest to most cyclists. These forces arise from passage through air (‘drag’) and interaction with the road surface (‘resistance’). Recent work recognises that resistance forces arise not only from the deformation of the tyre (‘rolling resistance’) but also from irregularities in the road surface (‘roughness resistance’), which lead to power dissipation in the body of the rider through vibration. The latter effect may also have an adverse impact on human health. In this work we offer a quantitative theory of roughness resistance and vibration that links these effects to a surface characterisation in terms of the International Roughness Index (IRI). We show that the roughness resistance and the Vibration Dose Value (or VDV, the usual vibration dosage metric) can be expressed in terms of elementary formulae. The roughness resistance depends only on the vertical stiffness of the bicycle and the roughness index. Surprisingly, other apparently relevant parameters, such as physiological characteristics of the bicycle rider and other features of the bicycle, do not enter. For roads of moderate roughness, roughness resistance is larger than rolling resistance. For very rough roads, roughness resistance is larger than aerodynamic drag. So only on roads of high quality (in most jurisdictions, accounting for less than 10% of the total) can roughness resistance be ignored. Roughness resistance can be mitigated by reducing the vertical stiffness of the bicycle. In common with other recent reports, we find that almost any cycling activity will breach public health guidelines relating to Vibration Dose Value.

Oral intake of milk ceramides improves skin hydration, elasticity, and wrinkles around the eyes: A 12-week, randomized, double-blind, placebo-controlled trial

Previous studies have shown that milk ceramide (MC) protects against photoaging in ultraviolet B-irradiated mice. Thus, we conducted a 12-week, randomized, double-blind, placebo-controlled clinical study to evaluate the skin improvement efficacy of MC. The participants were women aged 30–65 years with crow’s feet who were randomly assigned to receive either MC (600 mg/day) or a placebo for 12 weeks. The participants' degree of wrinkles around eyes was analyzed by expert graders. Additionally, changes in parameters related to skin wrinkling, moisture, and elasticity were measured. In the MC group, the wrinkles and roughness index around the eyes were significantly improved. After 12 weeks, the MC group showed significantly lower transepidermal water loss, and higher skin moisture and elasticity index than the placebo group. No serious adverse events were observed during the study period. Therefore, MC intake is expected to prevent skin aging by improving skin moisture and elasticity and weakening wrinkles.

Feasibility of Modernizing the Acceptable International Roughness Index Value

The U.S. National Highway System has set a standard for acceptable pavement roughness, measured by the international roughness index (IRI) at less than 2.68 m/km (170 in./mi). This value corresponds to a present serviceability rating (PSR) of 2.5, where half of the highway drivers perceive the pavement ride quality as unacceptable with a repair need. For over 3 decades, public transportation agencies in the U.S. have used this IRI value to determine when pavement surface rehabilitation is needed. However, with advancements in vehicle suspension systems improving ride quality, the current acceptable IRI value corresponding to 2.5 PSR has been questioned. To our knowledge, studies in the literature have yet to explore this issue. This study aims to evaluate the feasibility of modernizing the acceptable IRI value through nationwide, large-scale field testing, which is typically expensive and time-consuming. The IRI-present serviceability index (PSI) equation that is capable of converting the acceptable IRI value into the estimated 2.5 PSR (i.e., the PSI) was selected and calibrated by applying test quarter-car parameters synthesized through a literature review. Then, the calibrated IRI-PSI equation was used to find a new acceptable IRI value that was evaluated for the statistical significance and the financial benefits of the new value. The evaluation results suggest a need for a nationwide field test to modernize the current acceptable IRI value. Public transportation agencies can easily adapt the technical approach we used to calibrate the IRI-PSI equation without altering their current IRI measurement systems.

DISTRICT ROAD DATABASE MANAGEMENT BASED ON GEOGRAPHIC INFORMATION SYSTEM TO SUPPORT REGENCY ROAD MAINTENANCE (CASE STUDY: SINTANG DISTRICT)

Road maintenance on the section of the road classified as a district road in the Sintang sub-district is the responsibility of the Sintang District Government. The financial constraints of the Sintang District Government, allocated annually in the Regional Revenue and Expenditure Budget (APBD) of Sintang District, pose a challenge in realizing the plan for district road maintenance, whereas funding for road maintenance needs to be budgeted regularly to maintain the road conditions in good shape, therefore, in this case, stakeholders or decision-makers are expected to be able to consider and decide which road sections should receive recommendations for "urgent," "immediate," and "postponed" maintenance. The chosen road maintenance points must not solely prioritize the lowest construction costs, accessibility, or strategic area value. Indeed, in this case, an analysis is required to integrate various criteria, thus serving as the basis for a decision as the best choice. The research method is presented descriptively, explaining the determination of the scale for recommending maintenance of district roads. The data collection technique is carried out using primary data in the form of documentation photos of each road section and road condition data assessed using the Road Condition Index (RCI) method or road roughness index, as well as secondary data consisting of road inventory data, road condition data, accident data, area size data, socioeconomic data, and maps. The fulfillment of the Minimum Service Standards (MSS) for roads is then calculated based on the collected data. This includes both the MSS for individual road sections and the MSS for the road network. The fulfillment of the road network's MSS is divided into three aspects: accessibility, mobility, and safety. Based on the analysis of the MSS, it is categorized into three recommendations: "urgent," "immediate," and "postponed." After obtaining the recommendations for each road section, they are presented as a map. After obtaining the research results, two road sections fall under the "urgent" recommendation for road maintenance. For immediate recommendations, there are 15 road sections. Meanwhile, 44 road sections are included in the delayed recommendations for road maintenance to be carried out.