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The polarisation ratio of the linearly polarised light backscattered by fly ash particles is indicative of the carbon content. The determination of this parameter is useful to characterise the efficiency of coal burning furnaces. The main aim of this work is to design a single optical probe to measure the polarisation ratio. A probe has been built that can be bolted onto a furnace duct, and it has been tested on a wide range of ashes. A neural network analysis has also been explored for predicting carbon content.

This paper evaluates the benefits of harnessing artificial intelligence (AI) tools for safety compliance on construction projects in Nigeria.

This study employed a specialised approach by combining qualitative and quantitative approach. The study carried out a brief systematic literature review (SLR) to identify the variables of the study. These variables were prepared in a questionnaire which was distributed among professionals within the Nigerian construction sector using purposive sampling. A total of 140 questionnaires were retrieved. The collected data were analysed using Relative Importance Index (RII), Ginni’s Mean (GM) and exploratory factor analysis (EFA).

The analysis revealed that all the identified benefits hold considerable importance, with an average RII of 0.86, with real-time monitoring as the most prominent advantage. However, using the GM which was 0.861, the study identified “mitigation of hazards on worksites” as the stationary benefit of AI in safety compliance.

The study was conducted exclusively within Nigeria’s Federal Capital Territory, using a cross-sectional survey approach.

The results will be valuable for professionals and practitioners in the Nigerian construction sector, as they will acquire insights into the potential advantages of utilising AI tools for monitoring of safety compliance on construction projects.

The study adopted a robust approach by identifying the stationary benefit using the GM in combination with RII and EFA.