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The design for deconstruction (DfD) technique, a contemporaneous solution to demolition by optimizing disassembly activities to enable reuse, has recently emerged with several promises to promote the circular economy. However, little attention has been given to its implementation among design professionals, especially in the Global South. Therefore, this study aims to explore the drivers for DfD implementation among design professionals in the Ghanaian construction industry (GCI).

The study adopted a mixed research approach (explanatory sequential design) with an initial quantitative instrument phase, followed by a qualitative data collection phase. Data from the survey were analyzed using mean, standard deviation, one-sample t-Test, and normalization value (NV) test after a review of pertinent literature. These data were then validated through semistructured interviews with ten design professionals with in-depth knowledge of DfD.

The findings revealed that although all ten drivers are important, the eight key drivers for the DfD implementation were identified as, in order of importance, “Availability of computer software applications regarding DfD,” “Inclusion of DfD in the formal education of design professionals,” “Increasing public awareness of the concept of DfD,” “Organizing workshops/seminars for design professionals on the concept of DfD,” “Availability of DfD training,” “Regulation regarding DfD,” “Industry guidance regarding DfD” and “Establishing a market for salvaged construction components.”

This study's findings provide insights into an under-investigated topic in Ghana and offer new and additional information and insights into the current state-of-the-art on the factors that drive DfD implementation.

This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO₂ prediction models. The purpose of this paper is to pre-process a relatively large data of NO₂ from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO₂ prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist.

The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO₂ pollution.

This paper's hybrid model provides a basis for giving an informed decision on the NO₂ pollutant avoidance system.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration.