Auckpath Sawangsuriya

Effective road maintenance is critical for ensuring safety, efficiency, and convenience in transportation networks. However, conventional assessment methods, often relying on isolated metrics or visual inspections, fail to capture the complex interplay of pavement condition factors, leading to potential misallocation of maintenance efforts. This study proposes a two-stage framework to enhance road condition assessment and maintenance prioritization by integrating individual performance indices with a unified pavement assessment metric (UPAM) developed through the fuzzy analytic hierarchy process. In the first stage, road segments are pre-screened based on individual indices—the structural condition index, international roughness index, international friction index, and pavement condition index—to identify localized deficiencies. Segments are classified as high or low priority based on threshold performance. In the second stage, segments are systematically ranked within each group using UPAM scores. While individual indices offer detailed diagnostic insights, they lack consistency for ranking; conversely, the UPAM provides standardized prioritization but may obscure critical localized issues. The combined approach improves transparency, reliability, and decision-making effectiveness, offering a comprehensive tool for optimizing pavement maintenance strategies.

Road maintenance is essential for supporting road safety and user comfort. Developing predictive models for road surface conditions enables highway agencies to optimize maintenance planning and strategies. The international roughness index (IRI) is widely used as a standard for evaluating road surface quality. This study compares the performance of deep neural networks (DNNs) and graph convolutional networks (GCNs) in predicting IRI values. A unique aspect of this research is the inclusion of additional predictor features, such as the type and timing of recent roadwork, hypothesized to affect IRI values. Findings indicate that, overall, the DNN model performs similarly to the GCN model across the entire highway network. Given the predominantly linear structure of national highways and their limited connectivity, the dataset exhibits a low beta index, ranging from 0.5 to 0.75. Additionally, gaps in IRI data collection and discontinuities in certain highway segments present challenges for modeling spatial dependencies. The performance of DNN and GCN models was assessed across the network, with results indicating that DNN outperforms GCN when highway networks are sparsely connected. This research underscores the suitability of DNN for low-connectivity networks like highways, while also highlighting the potential of GCNs in more densely connected settings.

This paper presents the implementation of soil moisture and strength index measurements for earthwork construction quality control as well as a link between the in situ testing and structural property of earthen materials. Use of the convenient Dynamic Cone Penetrometer (DCP) in conjunction with conventional moisture-density measurements enhances quality control by achieving acceptable level of compaction, more uniform structural properties, and aids developing a controlled design parameter during the earthwork construction. Soil strength in term of DCP index normalized by the deviation of compaction moisture content from the optimum moisture content is proposed as performance criteria for a variety of engineered earth fills and special engineering assessment, prevention, and mitigation of geohazards e.g. earthen flood defense embankments.

The effect of median particle size and breadth of particle sizes in clean sands on the SWCC and the van Genuchten parameters α and n is described. For uniform sands, the air entry suction increases (α decreases) and the steepness diminishes slightly (n decreases) for the drying SWCC as the particles become finer. Increasing the breadth of particle sizes also increases the air entry suction (lower α) and reduces the steepness (lower n) of the SWCC. Breadth of the particle size distribution has a larger effect on steepness and n of the SWCC, and median particle size has a larger effect on the air entry suction and α. Similar effects occur for the wetting SWCC, although the impact on the water entry suction is less than the impact on the air entry suction. The systematic effects of median particle size and breadth of particle sizes on the SWCC are captured in a pedotransfer function (PTF) to estimate α and n for clean sand based on d₆₀ and C_u. Comparison with other SWCC data shows that α estimated with the PTF typically is within a factor of 2 of the actual α and n estimated with the PTF is within ± 2 of the actual n. These findings are applicable to α for sands with nonplastic fines provided the fines content is less than 20%, but apply to n only for sands with negligible fines content. A procedure to convert between van Genuchten and Brooks-Corey parameters for sands is also presented.

A compaction quality control (QC) in highway construction in Thailand is typically based on an in-place density measurement using the sand replacement method. Although monitoring compaction quality through a sand cone density test is relatively simple, such method is generally time consuming, labor intensive, less cost-effective, and destructive. This paper presents the laboratory evaluation of an innovative QC tool called the soil density gauge (SDG), which provides rapid and instantaneous measurement of density and moisture content based on the electrical impedance spectroscopy. The SDG was evaluated in a test box. Six highway materials were used in the study. The variation of SDG measurements along with its measurement characteristics was investigated herein. The paper also compared the SDG density and moisture content measurements with other tests such as the nuclear gauge, the soil stiffness gauge, and the sand cone method etc.