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Cracks on surface are often identified as one of the early indications of damage and possible future catastrophic structural failure. Thus, detection of cracks is vital for the timely inspection, health diagnosis and maintenance of infrastructures. However, conventional visual inspection-based methods are criticized for being subjective, greatly affected by inspector's expertise, labor-intensive and time-consuming.

This paper proposes a novel self-adaptive-based method for automated and semantic crack detection and recognition in various infrastructures using computer vision technologies. The developed method is envisioned on three main models that are structured to circumvent the shortcomings of visual inspection in detection of cracks in walls, pavement and deck. The first model deploys modified visual geometry group network (VGG19) for extraction of global contextual and local deep learning features in an attempt to alleviate the drawbacks of hand-crafted features. The second model is conceptualized on the integration of K-nearest neighbors (KNN) and differential evolution (DE) algorithm for the automated optimization of its structure. The third model is designated for validating the developed method through an extensive four layers of performance evaluation and statistical comparisons.

It was observed that the developed method significantly outperformed other crack and detection models. For instance, the developed wall crack detection method accomplished overall accuracy, F-measure, Kappa coefficient, area under the curve, balanced accuracy, Matthew's correlation coefficient and Youden's index of 99.62%, 99.16%, 0.998, 0.998, 99.17%, 0.989 and 0.983, respectively.

Literature review lacks an efficient method which can look at crack detection and recognition of an ensemble of infrastructures. Furthermore, there is absence of systematic and detailed comparisons between crack detection and recognition models.

This paper aims to review the status of development of building information modelling (BIM), its trends and themes across the six continents of the world.

A total of 914 journal articles sought from the search engine of Web of Science (WOS) based on the country/region option of the WOS to group them into continents. A best-fit approach was then applied in selecting the suitable software programmes for the scientometric analysis and comparisons and deductions were made.

The findings revealed that there are differences in the development of BIM across the six continents of the world. South America and Africa are lagging in the BIM research and Australia and Asia are growing, whilst Europe and North America are ahead. In addition, there exist differences in the research themes and trends in these continents as against the single view presented in extant studies.

This study introduced a new approach to carry out a comparative and taxonomic review and has provided both academic researchers and industrial practitioners with a clear status of development of BIM research and the trend across the six continents of the world.