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Conventional asphalt pavement is the dominant mode of passenger and freight traffic in Pakistan. As a result, asphalt pavements suffer from various failures, where rutting, corrugation, and fatigue cracking are significant. Fine aggregates and mineral fillers play a pivotal role in providing structural integrity in asphalt pavements when subjected to traffic and the environment. The current study aims to examine the effects of various locally accessible fine aggregate and mineral filler materials on the interlocking properties of asphalt mixtures in relation to internal friction angle, rutting resistance, and controlling environmental pollution as an indirect benefit, thereby reducing wastes. Four distinct asphalt samples were prepared using cinders, stone dust, natural sand, and surkhi as fine aggregates and mineral fillers, as a full replacement, as per ASTM D1559, confirming the Asphalt Institute’s gradation for asphalt wearing course. Optimum binder contents (OBC) of 4.40%, 4.1%, 6.57%, and 6.63% by weight of Marshall specimen were concluded for asphalt samples containing stone dust, natural sand, cinder, and surkhi, respectively. The results revealed that surkhi, natural sand, stone dust, and cinder all showed a diminishing tendency in developing interlocking properties in asphalt mixtures at internal friction angles of 35°, 33.7°, 32°, and 28.4°, respectively. The wheel tracking test results revealed that the asphalt samples made with surkhi as fine aggregates and fillers have the highest rut resistance, whereas samples made with cinders as fine aggregates and fillers have the lowest rut resistance. The direct shear test showed that fine aggregates with a larger angle of internal friction are significantly more stable in terms of rut resistance than fine aggregates with a smaller angle of internal friction. The current research will help to prevent pavement rutting and corrugation by adding surkhi into asphalt pavements, with the reduction in brick kiln waste providing an indirect benefit.

Road safety guidelines are not properly implemented and are not diverse enough to counter an annual increase in traffic volume. The mitigation techniques of road regulating bodies have failed in minimizing road accidents with the increase in road users. Therefore, the purpose of this study is to provide valuable insight to the facilitators and decision-making stakeholders by predicting the number of accidents because it is an existential hurdle toward the prevention of accidents. Therefore, this study aims to create temporal patterns to forecast the accident rates in Pakistan by utilizing univariate time series analysis such as seasonal autoregressive integrated moving average (SARIMA) and exponential smoothing (ES) models. The results indicate that the ES model fitted better on accident data over the SARIMA model after calculating the lowest mean absolute error, root mean square error, mean absolute percentage error and normalized Bayesian information criterion. The study provides the guiding principles to implement the forecasted accident rates in the designing of roads to ensure the safety of end users which is a prime interest for accident rate collection agencies, decision-makers, design consultants and accident prevention departments.

With time, construction waste is increasing massively and its dumping is a serious issue globally. Utilizing the waste in various products and construction projects is boosting, but still, the amount of waste is much higher. Transitions to more sustainable construction can assist in attaining the specific goal of slowing down natural resources depletion, reducing environmental damage by extracting and recycling new materials, and minimizing pollution from the processing, use, and disposal of materials once they complete their useful life period. An important way to do this is to improve efficiency and bring productivity in the utilization of resources. The circular economy is more productive and healthier, where raw materials are stored longer in the production cycle and can be recycled, thus producing less waste. Due to potential benefits through enhanced quality and productivity in the processes, the concept of circular economy is grabbing the attention of construction industry stakeholders to attain sustainable construction waste management. This chapter focuses on the significance of a circular economy for the attainment of sustainable waste management in the construction sector. Moreover, the impact of construction waste and its utilization through recent sustainable solutions which also impact the economy has also been highlighted.

The construction sector plays a significant role in contributing to uplifts in economic stability by generating employment and providing standardized social development. Economic sustainability in the construction sector has been less addressed despite its wide applicability in the economy. This study aimed to perform a comparative analysis to determine the application of a circular economy in the construction sector toward economic sustainability, along with its long-term forecasting. A time series analysis was used on the construction sector of the United States of America (USA), China, and the United Kingdom (UK) from 1970 to 2020, by taking into account individual effects to propose a framework with global validity. Statistical analysis was performed to analyze the dependence of the construction sector and determine its short- and long-term contributions. The results revealed that the construction sectors in these countries tend to bounce back to equilibrium in the case of short-term effects; however, the construction sector behaves differently with respect to each sector after experiencing long-term effects. The results show that the explanatory power of the forecasting model (R2) was found to be 0.997, 0.992, and 0.996 for the USA, China, and the UK. Based on the concept of the circular economy, it was concluded that the USA will become a leader in attaining sustainability in construction owing to its ability to recover quickly from shocks, and that the USA will become the largest construction sector in terms of GDP, with a USD 0.3 trillion higher GDP than that of the Chinese sector. Meanwhile, there will be no significant change in the construction GDP of the UK up to the end of 2050. Moreover, the speeds of the construction sector toward equilibrium in the long run in the USA, China, and the UK, and regaining of their original positions, is 0.267%, 1.04%, and 0.41% of their original positions, respectively. This study has a significance in acting as a guideline for introducing economic and environmental sustainability in construction policies, because of the potential of the construction sectors to recover from possible recessions in their respective countries.

International audience

Safety on construction sites is now a top priority for the construction industry all around the world. Construction labor is often seen as hazardous, putting employees at risk of serious accidents and diseases. The use of Industrial Revolution (IR) 4.0 advanced technologies such as robotics and automation, building information modelling (BIM), augmented reality and virtualization, and wireless monitoring and sensors are seen to be an effective way to improve the health and safety of construction workers at the job site, as well as to ensure construction safety management in general. The main aim of this research was to analyze the IR-4.0-related technologies for improving the health and safety problems in the construction industry of Malaysia by utilizing the analytical hierarchy process (AHP) technique. IR-4.0-related technologies show great potential in addressing the construction industry’s existing health and safety problems from the perspective of civil engineering practitioners and industry experts. This research adopted the analytical hierarchy process (AHP) for quantitative analysis of data collected through the survey questionnaire approach. The findings of the study indicate that from matrix multiplication, the highest importance among the criteria and the alternatives was for BIM with a score of 0.3855, followed by wireless monitoring and sensors (0.3509). This research suggests that building information modelling (BIM) and integrated systems had the greatest potential as advanced technology and should be prioritized when it comes to introducing it to the construction industry to improve the current health and safety performances.

The water level in a river defines the nature of flow and is fundamental to flood analysis. Extreme fluctuation in water levels in rivers, such as floods and droughts, are catastrophic in every manner, therefore, forecasting at an early stage would prevent possible disasters and relief efforts could be set up on time. This study aims to digitally model the water level in the Kabul River to prevent and alleviate the effects of any change in water level in this river downstream. This study used a machine learning tool known as the automatic autoregressive integrated moving average for statistical methodological analysis for forecasting the river flow. Based on the hydrological data collected from the water level of Kabul River in Swat, the water levels from 2011–2030 were forecasted, which were based on the lowest value of Akaike Information Criterion as 9.216. It was concluded that the water flow started to increase from the year 2011 till it reached its peak value in the year 2019–2020, and then the water level will maintain its maximum level to 250 cumecs and minimum level to 10 cumecs till 2030. The need for this research is justified as it could prove helpful in establishing guidelines for hydrological designers, the planning and management of water, hydropower engineering projects, as an indicator for weather prediction, and for the people who are greatly dependent on the Kabul River for their survival.

All over the world, increasing anthropogenic activities, industrialization, and urbanization have intensified the emissions of various pollutants that cause air pollution. Marble quarries in Pakistan are abundant and there is a plethora of small- and large-scale industries, including mining and marble-based industries. The air pollution caused by the dust generated in the process of crushing and extracting marble can cause serious problems to the general physiological functions of plants and it affects human life as well. Therefore, the objectives of this study were to assess the air quality of areas with marble factories and areas without marble factories, where the concentration of particulate matter in terms of total suspended particles (TSP) was determined. For this purpose, EPAM-5000 equipment was used to measure the particulate levels. Besides this, a spectrophotometer was used to analyze the presence of PM2.5 and PM10 in the chemical composition of marble dust. It was observed that the TSP concentrations in Darmangi and Malagori areas of Peshawar, Pakistan—having marble factories—were 626 µg/m3 and 5321 µg/m3 respectively. The (PM2.5, PM10) concentration in Darmangi was (189 µg/m3, 520 µg/m3) and in Malagori, it was recorded as (195 µg/m3, 631 µg/m3), which was significantly higher than the non-marble dust areas and also exceeded WHO recommended standards. It was concluded that the areas with the marble factories were more susceptible to air pollution as the concentration of TSP was significantly higher than the recommended TSP levels. It is recommended that marble factories should be shifted away from residential areas along with strict enforcement. People should be instructed to use protective equipment and waste management should be ensured along with control mechanisms to monitor particulate levels.

Reservoir water level (RWL) prediction has become a challenging task due to spatio-temporal changes in climatic conditions and complicated physical process. The Red Hills Reservoir (RHR) is an important source of drinking and irrigation water supply in Thiruvallur district, Tamil Nadu, India, also expected to be converted into the other productive services in the future. However, climate change in the region is expected to have consequences over the RHR’s future prospects. As a result, accurate and reliable prediction of the RWL is crucial to develop an appropriate water release mechanism of RHR to satisfy the population’s water demand. In the current study, time series modelling technique was adopted for the RWL prediction in RHR using Box–Jenkins autoregressive seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) hybrid models. In this research, the SARIMA model was obtained as SARIMA (0, 0, 1) (0, 3, 2)12 but the residual of the SARIMA model could not meet the autocorrelation requirement of the modelling approach. In order to overcome this weakness of the SARIMA model, a new SARIMA–ANN hybrid time series model was developed and demonstrated in this study. The average monthly RWL data from January 2004 to November 2020 was used for developing and testing the models. Several model assessment criteria were used to evaluate the performance of each model. The findings showed that the SARIMA–ANN hybrid model outperformed the remaining models considering all performance criteria for reservoir RWL prediction. Thus, this study conclusively proves that the SARIMA–ANN hybrid model could be a viable option for the accurate prediction of reservoir water level.