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To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries.

This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development.

ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals.

This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

Governments around the world instituted guidelines for calculating energy efficiency of vehicles not only by models, but by the whole universe of new vehicles registered. This paper compiles Multi-criteria decision-making (MCDM) studies related to automotive industry. We applied a Systematic Literature Review on MCDM studies published until 2015 to identify patterns on MCDM applications to design vehicles more fuel efficient in order to achieve full compliance with energy efficiency guidelines (e.g., Inovar-Auto). From 339 papers, 45 papers have been identified as describing some MCDM technique and correlation to automotive industry. We classified the most common MCDM technique and application in the automotive industry. Integrated approaches were more usual than individual ones. Application of fuzzy methods to tackle uncertainties in the data was also observed. Despite the maturity in the use of MCDM in several areas of knowledge, and intensive use in the automotive industry, none of them are directly linked to car design for energy efficiency. Analytic Hierarchy Process was identified as the common technique applied in the automotive industry.

In cities with high density, heat is often trapped between buildings which increases the frequency and intensity of heat events. Researchers have focused on developing strategies to mitigate the negative impacts of heat in cities. Adopting green infrastructure and cooling pavements are some of the many ways to promote thermal comfort against heat. The purpose of this study is to improve microclimate conditions and thermal comfort levels in high-density living conditions in Seoul, South Korea.

This study compares six design alternatives of an apartment complex with different paving and planting systems. It also examines the thermal outcome of the alternatives under normal and extreme heat conditions to suggest strategies to secure acceptable thermal comfort levels for the inhabitants. Each alternative is analyzed using ENVI-met, a software program that simulates microclimate conditions and thermal comfort features based on relationships among buildings, vegetation and pavements.

The results indicate that grass paving was more effective than stone paving in lowering air temperature and improving thermal comfort at the near-surface level. Coniferous trees were found to be more effective than broadleaf trees in reducing temperature. Thermal comfort levels were most improved when coniferous trees were planted in paired settings.

Landscape elements show promise for the improvement of thermal conditions because it is much easier to redesign landscape elements, such as paving or planting, than to change fixed urban elements like buildings and roads. The results identified the potential of landscape design for improving microclimate and thermal comfort in urban residential complexes.

The results contribute to the literature by examining the effect of tree species and layout on thermal comfort levels, which has been rarely investigated in previous studies.

Studies on the Transit-Oriented Development (TOD) for Bangkok are found sparingly. The TOD concept is a supportive development for the rapidly changing city in order to reduce urban transport problems while encouraging people to shift transport modes to use public transportations instead of private cars. This study discusses the context of TOD in the density, the design, and the diversity of land use around transit stations among successful stations in many countries. There were 18 station areas in Bangkok which, by using the TOD Readiness score, the assessment of the stations implies that the higher scoring transit stations are more compatible to supporting pedestrian use of the transit station with lower car dependency. The 4 top-scoring stations were assessing by using multinomial logistic regression model. The study has found TOD scores and the frequent uses of the stations consequently encourage the commuters around the station areas decided to rely on public transport instead of car dependency. This is an effort to overcome the understanding of the station areas by reducing the complexity of the TOD contexts to any transit station in Thailand to be eligible for future study.