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.

Purpose The construction industry is facing an enormous number of challenges due to continuous advancements in construction technologies and techniques. Hence, construction management theories have to confront critical newly issues concerning market globalization and construction innovations. The key factor to address these challenges is to ameliorate the competitive abilities of the competing construction firms. In this context, measuring competitiveness of construction firms is an efficacious approach to amplify their competitive growth and profitability. To this end, the purpose of this research paper is to design a three-tier multi-criteria decision making model for competitiveness assessment and benchmarking of construction companies, meanwhile tackling a wide range of essential factors and attributes that covers broad aspects of the present competitive market. Design/methodology/approach In the first tier, four new pillars (4P) of competitiveness assessment are introduced for construction firms, namely, organization performance, project performance, environment and client and innovation and development. These pillars are able to aid in construction firms’ management on both long and short term basis. Hence, 21 key competitive factors and eighty key competitive criteria are identified, incorporated and analyzed in this research study. The second tier encapsulates carrying out a questionnaire survey in the Canadian and Vietnamese market to garner two main sets of information. The first set of information incorporates responses of the pairwise comparisons between competitiveness factors and criteria. The second set involves gathering utility scores pertinent to each competitiveness criteria. The developed model then leverages the use of analytical hierarchy process to scrutinize the relative importance priorities of competitiveness factors and criteria. The third tier of the developed model encompasses the use of multi-attribute utility theory to compute competitiveness scores for construction companies through blending criteria’ relative importance weights alongside their respective utility functions. In addition, the third tier comprises conducting a sensitivity analysis to derive the most important criteria influencing the overall competitiveness of construction companies. The developed model is tested and validated using three case studies; one construction company from Canada and two construction companies from Vietnam. Findings Results demonstrated that the developed model has a potential to render a synthesized and methodical performance evaluation for the competitive ability of a given construction company. Furthermore, it was found that Vietnamese companies are more considerate towards pillars pertaining to environment and client while Canadian companies are more attentive towards innovation and development. The outcome of sensitivity analysis revealed that effectiveness of cost management highly affects the competitive ability of Vietnamese companies while effectiveness of cost management exhibits the most significant influence on the competitive of Canadian companies. Practical implications The developed model can benefit construction companies to understand their competitiveness in their market and diagnose their strengths and weaknesses. It is also can be useful in efficient utilization of their limited resources and development of sustainable and long-term strategic plans strategic plans, which consequently leads to maintaining better position in their dynamic business markets. Originality/value Literature review manifests that reported competitiveness assessment models and practices are not able to address present challenges, technologies and developments in construction market.

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Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf optimizer is exploited to improve the prediction accuracies of the Gaussian process through optimal estimation of its hyper parameters and to automatically interpret the significant deterioration factors. The second component involves three tiers of performance evaluation comparison, statistical significance comparisons, and consolidated ranking to assess the prediction accuracies of the developed GWO-GPR model. In this regard, the developed model is validated against six widely acknowledged machine learning models, which are back-propagation artificial neural network, Elman neural network, cascade forward neural network, generalized regression neural network, support vector machines, and regression tree. Results demonstrate that the developed GWO-GPR model significantly outperformed other deterioration prediction models in the five tunnel elements. In cast-in-place tunnel liners it accomplished a mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error of 1.65%, 0.018, 0.21%, 0.018, and 0.147, respectively. In this context, it was inferred that the developed GWO-GPR model managed to reduce the prediction errors of the back-propagation artificial neural network, Elman neural network, and support vector machines by 84.71%, 76.91%, and 69.6%, respectively. It can be concluded that the developed deterioration model can assist transportation agencies in creating timely and cost-efficient maintenance schedules of highway tunnels.