PERBANDINGAN METODE INTERNATIONAL ROUGHNESS INDEX DENGAN PAVEMENT CONDITION INDEX UNTUK PENENTUAN KONDISI JALAN NASIONAL DI KOTA WAMENA (STUDI KASUS : RUAS JALAN WAMENA – HABEMA)

Pembangunan infrastruktur jalan merupakan suatu kebutuhan mutlak bagi pengembangan suatu wilayah agar tercapai kesinambungan dan pemerataan pembangunan pada setiap daerah serta membentuk struktur ruang dalam rangka mewujudkan sarana pembangunan nasional. Untuk membuka isolasi dan akses masyarakat terhadap perkembangan perekonomian di Kota Wamena, pemerintah melaksanakan pembangunan dan pemeliharaan jalan. Pemeliharaan jalan bertujuan untuk mempertahankan tingkat pelayanan sesuai dengan standar pelayanan minimum yang ditetapkan.Penelitian ini dilakukan pada ruas Jalan Wamena-Habema sepanjang 35,100 kilometer. Penelitian ini bertujuan untuk membandingkan kondisi jalan hasil pengukuran metode IRI dengan metode PCI. Data yang digunakan berupa data IRI dan PCI semester 2 tahun 2020 yang diperoleh dari Sistem Pengelolaan Database Jalan Nasional (SiPDJN) Direktorat Jenderal Bina Marga Kementerian Pekerjaan Umum dan Perumahan Rakyat.Hasil penelitian menunjukkan ada perbedaan kondisi jalan Wamena-Habema berdasarkan metode IRI dan metode PCI. Pada metode IRI 61,8% kondisi baik, 32,2% kondisi sedang. Kondisi rusak ringan dan rusak berat 4,0% dan 2,0%. Sedangkan pada metode PCI 49,6% kondisi baik, 9,7% kondisi sedang. Kondisi jelek dan parah 40,5% dan 0,3%. Dengan dilakukan penelitian kondisi jalan menggunakan metode IRI dan PCI pada ruas Jalan Wamena-Habema dapat memberikan deskripsi atau gambaran tentang data kondisi jalan eksisting. Data kondisi jalan dapat digunakan sebagai database untuk perencanaan dan pelaksanaan pemeliharaan jalan.

A Study on the Roads Flatness Using International Roughness Index in New Taipei City

The quality of roads is an indicator of urban progress. The development of tourism and economy contributes to the increasing demands for transportation and, thus, aggravated burdens and vulnerability to damage of these roads, and the result is compromised transportation quality and safety. The Road Leveling Project is aimed to road updates and improvement of pavement quality. New Taipei City was selected as the subject for this study. International roughness index (IRI) was selected for field survey and statistical comparison. The outcome indicated that the IRI spread between 3.5 and 6.5 m/km before road leveling with an average of 4.71 m/km; the index fell between 2.5 and 4.5 m/km after road leveling with an average of 3.12m/km, suggesting that the IRI of the tested road sections showed a declining trend. For multi-lane road sections tested, the improvement was greater on the outer lanes than on the inner lanes. This proves that the implementation of the Road Leveling Project has made significant improvement in terms of pavement flatness. Suggestions are proposed in this study for the subsequent management and improvement polices of the Road Leveling Project, hoping that the pavement quality improvement continues to contribute to the extension of road service life and ride comfort.

International Roughness Index Modeling For Jointed Plain Concrete Pavement Using Artificial Neural Network

Climate attributes such as precipitation, extreme temperature, and freeze-thaw cycles along with traffic loads cause pavement distresses. The maintenance need for pavements is decided based on the pavement condition rating such as International Roughness Index (IRI). Generally, an IRI rating less than 2.68 m/km is acceptable, and a rating greater than 2.68 m/km is considered unacceptable and classified as “very poor” condition of the pavement. It is imperative to be able to accurately predict pavement conditions to prepare proper Maintenance and Rehabilitation (M&R) programs for the pavements. This study aims to develop IRI models that can successfully estimate the IRI values for Jointed Plain Concrete Pavement (JPCP) considering the M&R history of the pavements using Artificial Neural Networks (ANNs) approach. The study was carried out with the database collected from Long Term Pavement Performance (LTPP) program. The variables used for the ANN model development are initial IRI, pavement age, concrete pavement thickness, equivalent single axle load (ESAL), climatic region (wet-freeze, wet non-freeze, dry-freeze, dry non-freeze), construction number (CN), and several climatological data. After utilizing various ANN model structures, the best performing ANN model resulted in promising statistical measures (i.e. R2 = 0.87). The IRI prediction model can successfully estimate the increase of IRI values with the increase of ESAL value over time. The IRI prediction model can also estimate the decrease of IRI value after maintenance and rehabilitation. The predicted IRI values with good accuracy will help the local and state agencies to prepare for M&R programs for JPCP pavements and allocate a projected budget accordingly.

Roughness Modeling for Composite Pavements using Machine Learning

A large number of paved highway surfaces comprises composite pavements as a result of concrete pavement rehabilitation that uses an asphalt overlay on top of the concrete surface. Annually, billions of dollars are spent on the maintenance and rehabilitation of road networks. Roughness is one of the several indicators of road conditions used to make objective decisions related to road network management. The irregularities in the pavement surface affecting the ride quality of road users can be described by a standard roughness index defined as the International Roughness Index (IRI). Roughness prediction models can identify rehabilitation needs, analyze rehabilitation effects, and estimate future pavement conditions to implement different Maintenance and Rehabilitation (M&R) activities to extend the pavement life cycle and provide a smooth surface for road users. This study intended to develop pavement performance models to predict roughness for asphalt overlay on concrete pavement sections using the Long-Term Performance Pavement (LTPP) program database. Artificial Neural Networks (ANNs) approach was used to develop roughness prediction models. A total of 52 pavement sections with 592 data points were analyzed. Five models were developed, and the best performing model, Model 5 was found with an average square error (ASE) of 0.0023, mean absolute relative error (MARE) of 12.936, and coefficient of determination (R2) of 0.88. Model 5 utilized one output variable (IRIMean) and 14 input variables (i.e., Initial IRIMean, Age, Wet-Freeze, Wet Non-Freeze, Dry-Freeze, Dry Non-Freeze, Asphalt Thickness, Concrete Thickness, CN Code, ESAL, Annual Air Temperature, Freeze Index, Freeze-Thaw, and Precipitation). The ANN model structure utilized for Model 5 was 14-9-1 (14 inputs, 9 hidden nodes, and 1 output). Environmental impacts and traffic repetitions can cause severe damage to the pavement if timely maintenance and rehabilitation are not performed. By considering the effects of the M&R history of the pavement, it is possible to obtain realistic prediction models for future planning. Therefore, the developed ANN roughness performance models in this paper can be used as a prediction tool for IRI values and guide decision-makers to develop a better M&R plan. Local and state agencies can use available historical traffic and climatological data in the developed models to estimate the change in IRI values. Utilizing these prediction models eliminates time-consuming data collection and post-processing, and consequently, a cost reduction. This low-cost tool will improve the condition assessment and effective M&R scheduling.

The spatial distribution of the woodland communities and their associated environmental drivers in the Golden Gate Highlands National Park, South Africa

The extreme variability in the topography, altitude and climatic conditions in the temperate Grassland Mountains of Southern Africa is associated with the complex mosaic of grassland communities with pockets of woodland patches. Understanding the relationships between plant communities and environmental parameters is essential in biodiversity conservation, especially for current and future climate change predictions. This article focused on the spatial distribution of woodland communities and their associated environmental drivers in the Golden Gate Highlands (GGHNP) National Park in South Africa. A generalized linear model (GLM) assuming a binomial distribution, was used to determine the optimal environmental variables influencing the spatial distribution of the woodland communities. The Coefficient of Variation (CV) was relatively higher for the topographic ruggedness index (68.78%), topographic roughness index (68.03), aspect (60.04%), coarse fragments (37.46%) and the topographic wetness index (31.33) whereas soil pH, bulk density, sandy and clay contents had relatively less variation (2.39%, 3.23%, 7.56% and 8.46% respectively). In determining the optimal number of environmental variables influencing the spatial distribution of woodland communities, roughness index, topographic wetness index, soil coarse fragments, soil organic carbon, soil cation exchange capacity and remote-sensing based vegetation condition index were significant (p 0.05) and positively correlated with the woodland communities. Soil nitrogen, clay content, soil pH, fire and elevation were also significant but negatively correlated with the woodland communities. The area under the curve (AUC) of the receiver operating characteristics (ROC) was 0.81. This was indicative of a Parsimonious Model with explanatory predictive power for determination of optimal environmental variables in vegetation ecology.Conservation implications: The isolated woodland communities are sources of floristic diversity and important biogeographical links between larger forest areas in the wider Drakensberg region. They provide suitable habitats for a larger number of forest species and harbour some of the endemic tree species of South Africa. They also provide watershed protection and other important ecosystem services. Understanding the drivers influencing the spatial distribution and persistence of these woodland communities is therefore key to conservation planning in the area.