The construction sector exerts an exceptional impact on economic development all over the world. Adequate buildings and infrastructures made by the construction sector ensure that a country reaches certain targets like social development, industrialization, freight transportation, sustainable development, and urbanization. This study aims to determine the construction sector’s connectivity with other sectors through complex linkages that contribute immensely to the economy and gross domestic product (GDP). The data were collected from the Department of Statistics Malaysia and the World Bank from the year 1970 to 2019, and the Pearson correlation test, the cointegration test, and the Granger causality test were conducted. The vector error correction model (VECM) was created for short-term and long-term equilibrium analysis and impulse response function (IRF) was performed to study construction industry behavior. Afterwards, the forecasting was done for the year 2020 to 2050 of the Malaysian economy and GDP for the required sectors. It was revealed that some sectors, such as agriculture and services, have forward linkages while other sectors, such as manufacturing and mining, are independent of construction sector causality, which signifies the behavior of the contributing sectors when a recession occurs, hence generating significant revenue. The Malaysian economy is moving towards sustainable production with more emphasis on the construction sector. The outcome can be used as a benchmark by other countries to achieve sustainable development. The significance of this study is its usefulness for experts all over the world in terms of allocating resources to make the construction sector a sustainable sector after receiving a shock. A sustainable conceptual framework has been suggested for global application that shows the factors involved in the growth of the construction industry to ensure its sustainable development with time.

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.

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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.

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.

Vehicle accidents take human life all over the world particularly in developing countries like Pakistan. It is estimated that 1.2 million people lose their lives in road accidents every year. Apart from this, 20 to 50 million are injured on a yearly basis. This annual increase in the traffic accidents trend is alarming. To bring improvement in the current road network system, the specialists need to analyze the historical data of road crashes of an area. This research aims to use the visualization technique to have a better understanding of the accident data. This study uses the data of Peshawar, Pakistan, where the raw data were first organized, filtered, pre-processed and finally, visualization was performed to construct a systemic and homogenous data model. Various infographics were produced with the help of different software interface and visualization options. It was revealed that most of the accidents occur in the daytime and with those people who do not have enough traffic education. The 30 to 45 years age group was more active in causing the accidents. Therefore, the behaviour of this age group of drivers needs further investigation. This study will be useful for concerned authorities in devising an efficient mechanism to alleviate road accident cases.

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.

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.