Construction and demolition waste treatment has become an increasingly pressing economic, social, and environmental concern across the world. This study employs a science mapping approach to provide a thorough and systematic examination of the literature on waste management research. This study identifies the most significant journals, authors, publications, keywords, and active countries using bibliometric and scientometric analysis. The search retrieved 895 publications from the Scopus database between 2001 and 2021. The findings reveal that the annual number of publications has risen from less than 15 in 2006 to more than 100 in 2020 and 2021. The results declare that the papers originated in 80 countries and were published in 213 journals. Review, urbanization, resource recovery, waste recycling, and environmental assessment are the top five keywords. Estimation and quantification, comprehensive analysis and assessment, environmental impacts, performance and behavior tests, management plan, diversion practices, and emerging technologies are the key emerging research topics. To identify research gaps and propose a framework for future research studies, an in-depth qualitative analysis is performed. This study serves as a multi-disciplinary reference for researchers and practitioners to relate current study areas to future trends by presenting a broad picture of the latest research in this field.

Developing successful municipal waste management planning strategies is crucial for implementing sustainable development. The research proposed the application of an optimized artificial neural network (ANN) to forecast quantities of waste in Poland. The neural network coupled with particle swarm optimization (PSO) algorithm is compared to the conventional neural network using five assessment metrics. The metrics are coefficient of efficiency (CE), Pearson correlation coefficient (R), Willmott’s index of agreement (WI), root mean squared error (RMSE), and mean bias error (MBE). Selected explanatory factors are incorporated in the developed models to reflect the influence of economic, demographic, and social aspects on the rate of waste generation. These factors are population, employment to population ratio, revenue per capita, number of entities by type of business activity, and number of entities enlisted in REGON per 10,000 population. According to the findings, the ANN–PSO model (CE = 0.92, R = 0.96, WI = 0.98, RMSE = 11,342.74, and MBE = 6548.55) significantly outperforms the traditional ANN model (CE = 0.11, R = 0.68, WI = 0.78, RMSE = 38,571.68, and MBE = 30,652.04). The significant level of the reported outputs is evaluated using the Wilcoxon–Mann–Whitney U-test, with a significance level of 0.05. The p-values of the pairings (ANN, observed) and (ANN, ANN–PSO) are all less than 0.05, suggesting that the models are statistically different. On the other hand, the P-value of (ANN–PSO, observed) is more than 0.05, suggesting that the difference between the models is statistically insignificant. Therefore, the proposed ANN–PSO model proves its efficiency at estimating municipal solid waste quantities and may be regarded as a cost-efficient method of developing integrated waste management systems.

Heritage buildings are significant historical and architecture added value, which requires deep and precise preliminary brainstorming when considering upgrading or retrofitting these valuable buildings. In this study, we opted to highlight some passive design architecture interventions to improve the thermal comfort and the required cooling energy for buildings. The Murabba Palace in Riyadh was selected as a case study. DesignBuilder software was used to evaluate the energy performance of ten passive architectural design alternatives throughout different seasons in an attempt to improve the energy performance and thermal comfort of heritage buildings. The ten passive design scenarios encompassed double low-E glass, double reflected glass, double low-E glass and double wall with an air gap, double low-E glass and double wall with thermal insulation, double low-E glass and double wall with lightweight thermal insulation, double low-E glass and double wall with sprayed foam insulation, double reflected glass and double wall with an air gap, double reflected glass and double wall with thermal insulation, double reflected glass and double wall with lightweight thermal insulation, and double reflected glass and double wall with sprayed foam insulation. The results show that using double low-E glass and applying a double wall with polystyrene thermal insulation can enhance the thermal comfort inside the building and reduce the energy performance and CO₂ emissions to 17% and 9%, respectively.

Oil and gas pipelines are lifelines for a country’s economic survival. As a result, they must be closely monitored to maximize their performance and avoid product losses in the transportation of petroleum products. However, they can collapse, resulting in dangerous repercussions, financial losses, and environmental consequences. Therefore, assessing the pipe condition and quality would be of great significance. Pipeline safety is ensured using a variety of inspection techniques, despite being time-consuming and expensive. To address these inefficiencies, this study develops a model that anticipates sources of failure in oil pipelines based on specific factors related to pipe diameter and age, service (transported product), facility type, and land use. The model is developed using a multilayer perceptron (MLP) neural network, radial basis function (RBF) neural network, and multinomial logistic (MNL) regression based on historical data from pipeline incidents. With an average validity of 84% for the MLP, 85% for the RBF, and 81% for the MNL, the models can forecast pipeline failures owing to corrosion and third-party activities. The developed model can help pipeline operators and decision makers detect different failure sources in pipelines and prioritize the required maintenance and replacement actions.

Purpose: Heritage buildings are significant for their historical and architectural value. Due to the lack of rating systems designed specifically for heritage buildings, it is essential to develop and validate a heritage building assessment tool that considers its specific characteristics. The purpose of this study is to provide an extensive review of research on Sustainability of Heritage Buildings (SHBs).Design/methodology/approach: This review highlights methodologies applied in SHBs research and analyzes major global rating systems in order to identify their deficiencies for SHBs assessment. A systematic review was employed and articles from the top 10 high impact factor journals were studied. Twelve major global rating systems and their assessment criteria were identified.Findings: Significant variability was observed among the assessment tools since each tool assesses several criteria, factors and indicators that fit its local context. Part of this variability can also be seen in the rating scales, threshold values and accreditation titles. As a result, the final sustainability ranking for a given building cannot be compared among the 12 rating systems. Most importantly, these systems fail to analyze some factors such as energy that are considered important with respect to heritage building assessment.Originality/value: Since no specific rating system could be identified in this review as the most appropriate for heritage buildings, a new sustainability assessment tool that is specific to heritage buildings should be developed. Such a tool will enable facility managers to evaluate and improve the sustainability of their heritage buildings while preserving them.