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Notwithstanding the Geographical Information System (GIS) being a fast-emerging green area of a digital revolution, the available studies focus on different subject areas of application in the construction industry, with no study that clarifies its knowledge strands. Hence, this systematic review analyses GIS core area of application, its system integration patterns, challenges and future directions in the construction industry.

A systematic review approach was employed, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. A total of 60 articles published between 2011 and 2022 were identified, thoroughly reviewed and analysed using thematic analysis.

The analysis revealed spatial planning and design, construction-task tracking, defect detection and safety monitoring as its four main application-based areas. The findings showed that the adoption of GIS technology is rapidly expanding and being utilised more in building projects to visual-track construction activities. The review discovered an integrated pattern involving data flow from a device and window-form application to GIS, the pathways to data exchange between platforms to platforms, where ArcGIS is the most used software. Furthermore, the study highlighted the lack of interoperability between heterogeneous systems as the crux impediment to adopting GIS in the built environment.

The research provides a deep insight into possible areas where GIS is adopted in the construction industry, identifying areas of extensive and limited application coverage over a decade. Besides, it demystifies possible pathways for future integration opportunities of GIS with other emerging technologies within the construction industry.

Several factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.

The research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).

The research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.

Data were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.

The research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.

The research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

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.