The construction industry has a significant impact on the environment. It is responsible for a large proportion of global CO2 emissions, as well as the use of both natural resources and energy reserves. Green building (GB) practices are becoming increasingly recognized as a way of mitigating CO2 emissions and energy consumption, with environmental, economic, financial, social and benefits accruing. This paper reports the results of a study about the opportunities to promote sustainable construction (SC) practices in Kuwait. This entails identifying the current status of SC practices, the awareness level and knowledge of construction stakeholders, the key drivers that motivate the implementation of green practices, as well as barriers to SC. After comprehensive reviewing the literature, the data is conducted and collected through quantitative approach by questionnaires survey. The judgmental and snowball sampling techniques are used for the data collection. The local stakeholders associated with the Kuwait construction industry were the target population and used as a platform to obtain the data. A total of 678 questionnaires were distributed and 504 completed questionnaires were received. The quantitative data is then analyzed through descriptive analysis and inferential statistics. The main findings of this research are as follows: implementation of the concept of SC is low in the Kuwaiti construction industry; more action and strategies to improve and encourage this concept are therefore required in order for it to be applied efficiently in future projects; lack of awareness was found to be the main barrier to the use of SC approaches in Kuwait; the Kuwaiti government must take initiatives in terms of introducing standards, policies and incentives to promote SC. The findings of this study provide valuable information for organizations that intend to participate in green construction projects in Kuwait

Barriers to Implementation Sustainable Cement Manufacturing in Kuwait

Urban infrastructure drives economic growth and sustainability, yet cities like Kuwait face challenges from aging systems, urbanization, and climate change. Given Kuwait's oil-dependent economy, resilient infrastructure is key for diversification. This chapter explores asset management strategies, particularly Structural Health Monitoring (SHM), to enhance efficiency and sustainability. Kuwait's major projects, like metro systems and renewable energy, require effective management. SHM integration enables predictive maintenance, reduces costs, and extends infrastructure lifespan. IoT, AI, and remote sensing optimize monitoring and resource allocation. Despite its benefits, SHM adoption faces policy gaps, financial constraints, and institutional resistance. Addressing these requires reforms, investment, and public-private collaboration. Embracing SHM will strengthen Kuwait's infrastructure, attract investment, and drive sustainable urban development.

Severe ice accretion on transmission lines can disrupt electrical grids and compromise the stability of power systems. Consequently, precise prediction of ice coating on transmission lines is vital for guiding their operation and maintenance. Traditional single-model icing prediction methods often exhibit limited accuracy under varying environmental conditions and fail to yield highly accurate predictions. We propose a multi-scenario, two-stage adaptive ensemble strategy (MTAES) for ice coating prediction to address this issue. A combined clustering approach is employed to refine the division of ice weather scenarios, segmenting historical samples into multiple scenarios. Within each scenario, the bagging approach generates multiple training subsets, with the extreme learning machine (ELM) used to build diverse models. Subsequently, a two-stage adaptive weight allocation mechanism is introduced. This mechanism calculates the distance from the scenario cluster centers and the prediction error of similar samples in the validation set for each test sample. Weights are dynamically allocated based on these data, leading to the final output results through an adaptive ensemble from the base model repository. The experimental results show that the model is significantly better than traditional models in predicting ice thickness. Key indicators of RMSE, MAE, and ${\mathrm{R}}^2$ reach 0.675, 0.522, and 83.2%, respectively, verifying the effectiveness of multi-scene partitioning and adaptive weighting methods in improving the accuracy of ice cover prediction.