State-of-the-Art Review on Utilization of Fly Ash in Pavement Structures

The use of fly ash in construction has been on the rise, yet its application in pavement construction remains relatively underexplored. This study addresses this gap by critically reviewing 70 years of research on fly ash usage in pavement engineering, offering valuable recommendations. Class 'C' fly ash is employed for soil stabilization, while class 'F' is used in concrete. In both flexible (asphalt) and rigid (concrete) pavements, fly ash primarily functions as a filler material. Fine ash, owing to its fineness, enhances asphalt concrete by reducing void ratios and water sensitivity, as well as easing subgrade compaction while increasing compressive strength. Incorporating fly ash into Hot Mix Asphalt (HMA) enhances resistance to cracking and oxidative ageing. Adding fly ash (up to 25%) significantly boosts soil failure stress and strain values by 106% and 50%, respectively, while a combination of 8% lime and 18% fly ash yields maximum shear strength. A modest amount of lime (1-2%) mixed with 10% fly ash achieves a maximum dry density of 1.98 gm/cm3 at an optimal water content of 12.62%. Additional testing by researchers corroborates and validates the findings of this literature review

NEED FOR SMART MOBILITY AND CHALLENGES AND OPPORTUNITIES FOR TRANSITIONING TOWARD IT IN CAR-DEPENDENT COUNTRIES: INSIGHTS FROM LITERATURE

Car dependence is a trend brought about by the desire for comfortable transportation, in many countries around the world. After the invention and acceptance of automobiles, cities were designed with layouts that favored automobiles at the expense of other forms of transportation. However, the situation has changed with research and execution of plans for smart cities, with smart mobility transition taking centre stage. The purpose of this research is to highlight the need for transition to smart mobility, provide detailed description of various aspects of smart mobility and analyse the challenges and opportunities associated with the transition to smart mobility in car-dependent countries. A thorough and critical review of the literature has been done to achieve the aim of this study. Previous research efforts indicated that car-dependent cities have experienced several challenges in their transition to smart mobility, including inadequate infrastructure, low acceptance of new technological solutions, inadequate knowledge and framework for big data, financial constraints, data quality management, integration of data from different sources, privacy issues, and development of appropriate of government policies. There are several promising recommendations, which implementation is expected to help car-dependent countries overcome the above challenges and open opportunities for a successful transition. These recommendations include implementation of aggressive government policies, practicing greater inclusivity, and planning for the future of smart mobility by investing in Internet of Things (IoT) applications and reliable infrastructure. To facilitate the decision makers, challenges have been mapped with recommendations for transition to smart mobility, in light of the review findings

Modelling of Asphalt's Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis

The modification by polymers and nanomaterials can significantly improve different properties of asphalt. However, during the service life, the oxidation affects the constituents of modified asphalt and subsequently results in deviation from the desired properties. One of the important properties affected due to oxidation is the adhesive properties of modified asphalt. In this study, the adhesive properties of asphalt modified with the polymers (styrene-butadiene-styrene and styrene-butadiene) and carbon nanotubes were investigated. Asphalt samples were aged in the laboratory by simulating the field conditions, and then adhesive properties were evaluated by different tips of atomic force microscopy (AFM) following the existing functional group in asphalt. Finally, a predictive modelling and machine learning technique called the classification and regression tree (CART) was used to predict the adhesive properties of modified asphalt subjected to oxidation. The parameters that affect the behaviour of asphalt have been used to predict the results using the CART. The results obtained from CART analysis were also compared with those from the regression model. It was observed that the CART analysis shows more explanatory relationships between different variables. The model can predict accurately the adhesive properties of modified asphalts considering the real field oxidation and chemistry of asphalt at a nanoscale.publication of this article was funded by the Qatar National LibraryScopu

Random forest models for motorcycle accident prediction using naturalistic driving based big data

Motorcycle accident studies usually rely upon data collected from road accidents collected through questionnaire surveys/police reports including characteristics of motorcycle riders and contextual data such as road environment. The present study utilizes big data, in the form of vehicle trajectory patterns collected through GPS, coupled with self-reported road accident information along with motorcycle rider characteristics to predict the likelihood of involvement of a motorcyclist in an accident. Random Forest-based machine learning algorithm is employed by taking inputs based on a variety of features derived from trajectory data. These features are mobility-based features, acceleration event-based features, aggressive overtaking event-based features and motorcyclists socio-economic features. Additionally, the relative importance of features is also determined which shows that aggressive overtaking event-based features have more impact on motorcycle accidents as compared to other categories of features. The developed model is useful in identifying risky motorcyclists and implementing safety measures focused towards them

Who is the bigger culprit? Studying impacts of traffic and land use on noise levels in CBD area of Karachi, Pakistan

The trend of urbanization has attracted increased attention towards urban areas around the world. Central business districts (CBDs) serve as the core of commercial activities of urban areas and are often associated with high-density population. Noise pollution in urban areas, especially CBDs, is considered as an important issue for planners and policy makers especially with regard to human health. Noise prediction models for CBD area of Karachi, Pakistan, have been developed in this study using land use and traffic parameters. These models show that traffic and built-up space (especially residential land use) contribute positively and vacant space contributes negatively to the noise levels. However, it was found that traffic volume has higher impact, than land use, in terms of on noise levels in CBD area. The models of this research are anticipated to be used for planning of urban CBD areas in other cities where noise levels do not meet international health standards. In addition, they would be useful in calculating the rate of traffic volume associated with residential land use

Energy Consumption Trends in Energy Scarce and Rich Countries: Comparative Study for Pakistan and Saudi Arabia

Energy crisis is raising serious concerns throughout the world. There has been constant rise in energy consumption corresponding to the increase in global population. This sector affects the other pillars of national economy including industries and transportation. Because of these reasons, the traditional fossil-based energy sources are depleting rapidly, resulting in high and unstable energy prices. Saudi Arabia and Pakistan, although different from each other in terms of their economic stability and political systems, still rely heavily on the traditional fossil fuels. This paper presents the comparison of these two countries in terms of their energy consumption and factors affecting it. These factors include, but not limited to, economic development, and growth in population and other sectors such as; industries, transportation, etc. The comparison is also made with the regional and global energy consumption trends and these countries. Moreover, regression models were built to predict energy consumption till 2040 and compare the growth in this sector and share in global energy demand. Energy consumption in oil-rich countries (Saudi Arabia) has been driven through its economic development, while for energy insecure country (Pakistan) it is mainly because of population growth. It was also found that in the next two decades the share of Pakistan in the global energy demand will increase. This concludes that population growth will have more impact on energy consumption than economic growth. It could mean that the shift in energy sector would shift towards sustenance instead of using energy for commercial or industrial usage. Conference Track: Policy and Finance and Strategie

Effect of Organizational Structures and Types of Construction on Perceptions of Factors Contributing to Project Failure in Pakistan

The construction industry is viewed as the regulator of national economy globally. Its importance in Pakistan has increased greatly because of the involvement of international funding agencies in infrastructure projects. The feasibility of such projects is largely dependent upon the satisfaction of multiple stakeholders. Hence, it is important to explore the factors considered by different construction organizations in evaluating the failure of their projects. This study focuses on providing a rating of factors affecting construction project failures in the construction industry of Karachi. In addition to that, difference in perception between professionals belonging to different construction projects and organizational structures has also been evaluated. The results of this study show that factors related to project planning and management are rated higher by professionals in general. Secondly, it was also observed that client satisfaction and its related factors were rated higher by organizations with projectbased management structures

Involvement of Road Users from the Productive Age Group in Traffic Crashes in Saudi Arabia: An Investigative Study Using Statistical and Machine Learning Techniques

Road traffic crashes (RTCs) are a major problem for authorities and governments worldwide. They incur losses of property, human lives, and productivity. The involvement of teenage drivers and road users is alarmingly prevalent in RTCs since traffic injuries unduly impact the working-age group (15–44 years). Therefore, research on young people’s engagement in RTCs is vital due to its relevance and widespread frequency. Thus, this study focused on evaluating the factors that influence the frequency and severity of RTCs involving adolescent road users aged 15 to 44 in fatal and significant injury RTCs in Al-Ahsa, Saudi Arabia. In this study, firstly, descriptive analyses were performed to justify the target age group analysis. Then, prediction models employing logistic regression and CART were created to study the RTC characteristics impacting the target age group participation in RTCs. The most commonly observed types of crashes are vehicle collisions, followed by multiple-vehicle and pedestrian crashes. Despite its low frequency, the study area has a high severity index for RTCs, where 73% of severe RTCs include individuals aged 15 to 44. Crash events with a large number of injured victims and fatalities are more likely to involve people in the target age range, according to logistic regression and CART models. The CART model also suggests that vehicle overturn RTCs involving victims in the target age range are more likely to occur as a result of driver distraction, speeding, not giving way, or rapid turning. As compared with the logistic regression model, the CART model was more convenient and accurate for understanding the trends and predicting the involvement probability of the target age group in RTCs; however, this model requires a higher processing time for its development

Noise level based equivalency factors for different mobility options within heterogeneous traffic flow

This study employs a novel approach of comparing the effects of different types of mobility options on noise levels around arterials with the use of noise-level based passenger car unit (PCU) factors. The study area of this research was Karachi, Pakistan, although the methodology can be applied elsewhere. A regression model was developed for calculating PCUs based on noise level. To ensure maximum spread of data collection and variance in levels of traffic presence, the data was collected from five arterials of Karachi. Traffic volume count included mobility options of motorbikes, cars, pickup, rickshaws, buses, and trucks. It was found that the noise-based PCU factors differ greatly compared to those calculated based on traffic flow. Highest noise based PCU factor was for trucks, used for freight mobility, and lowest one was for rickshaws, a very common shared mobility option for passengers. These equivalency factors can provide a convenient approach for prediction of noise levels, along arterials, for transportation planners at the project development stage. Consequently, they can be utilized for planning and development of sustainable and healthy communities. Their importance is also justified on the basis of recent trend which includes introduction of various shared mobility options in Karachi, such as metro bus, online delivery, and ride-hiring services

Budget and cost contingency CART models for power plant projects

Cost overruns are a ubiquitous feature of construction projects, and realistic budgeting at the development stage plays a significant role in their control. However, the application of existing models to budgeting for power plant projects is restricted by the limited amount of project-specific cost data available. This study overcomes this by using a Classification and Regression Tree (CART) approach involving mixed methods of website visits, document study, and expert opinion to predict the amount of project cost (PC) and cost contingency (CC) needed to cover probable cost increases by the use of models containing readily available project attributes and national economic parameters at the project development stage. The modeling process is demonstrated and tested with a case study involving 58 Bangladeshi power plant projects – producing average absolute errors ranging from 0.7% to 1.7% and enabling project cost, inflation rate, and GDP to be identified as significant factors affecting PC and CC modeling. The approach can be applied to predict the PC during preliminary budgeting and selecting a project type and location aligned to the country’s economic status and policy-making strategies, thus facilitating further investment decisions