The International Roughness Index (IRI) is a widely used measure of pavement roughness that has important implications for vehicle safety, ride quality, and road maintenance. Over the years, many research studies have been conducted on IRI, but the literature is dispersed and lacks an overall research mapping. To address this issue, a Systematic Literature Network Analysis (SLNA) method to review the topic of IRI from scientific publications from 2000-2021 to obtain state-of-the-art mapping, trending topics, and future work projection. The results show a significant increase in scientific document publications. Network visualization contains 189 keywords divided into six clusters. The biggest cluster is focuses on measuring road surface conditions to obtain the IRI value as part of monitoring road surface conditions using a mechanical method and vibration response. The keywords featured on the word cloud are pavements, surface roughness, road and street, pavement performance, asphalt pavement, and concrete pavement. Top trend topics are predictive analytics, decision trees, machine learning, and roughness prediction. The keywords machine learning and learning algorithms are up-to-date topics and closely related to forecasting and the international roughness index. The IRI prediction model is still feasible for further research by using a machine learning prediction model.
At the initiation and design stage, the stakeholders (owners and planners) are faced with the choice to determine which type of pavement will be used in accordance with the budget provided yet they must also consider for the long-life maintenance and the environmental aspect the of the road pavement project. One indicator of an environmentally friendly road pavement project is that it must be low in energy and low in emissions. The aims of this study are to conduct a literature review to determine the extent of the previous research that have been carried out, as well as the consumption of energy in the construction projects. The results will be used as a basis for developing critical thinking and conceptual frameworks to fill the research gaps and provide novelty for further research. The study was conducted using a qualitative bibliometric analysis method by identifying selected academic publications from Scopus database with the use of VOSviewer software to process the data. The results of this study indicate that there is an opportunity to conduct research in order to develop energy optimization models for green and sustainable road construction projects from the design, construction, operation and maintenance stages.
LCA has been utilized over the past two decades to estimate the environmental impacts of pavement in infrastructures. The purpose of this study is to systematically map research on the use of LCA to calculate energy and emissions in road infrastructure projects. The research method is carried out by a literature review, in terms of systematic mapping study of a number of previous scientific publications, in the form of documents that have been published in international and national journals and proceedings, etc., in the last thirty years. The results show that: The topic of LCA is still an interesting area of research, and the trend from year to year shows an increase in the publication of articles in reputable journals. As much as 57.8% research, using the process based calculation method. Only 15.6% of research calculated energy and emissions in the four completed stages of the project life cycle. As much as 37.5% research compared the flexible and rigid pavement as research objects. There is a chance to research the development of the energy optimization model for road infrastructure projects using cradle to cradle system boundary, from initiation to the end of life as a whole project life cycle.
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