Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning. A total of 30 research articles have been collected for the review which is published in top tier journals and conferences in the past decade. A comprehensive analysis and comparison of these methods are performed to highlight the most promising automated approaches for crack detection.

Since the 1980s, smart buildings have aroused the interest of researchers. However, there is still no consensus on what the intelligence of a building is, and what enhances that intelligence. The purpose of this paper is to identify and correlate the main drivers and systems of smart buildings, by associating them with the main beneficiaries: users, owners, and the environment. To identify the main drivers and systems of these buildings, we carried out a comprehensive, detailed, and interpretative literature search. From the selected articles, we sorted the information, extracted the main concepts and knowledge, and, finally, identified the set of potential drivers and systems. Results showed eleven drivers and eight systems, and these can be enhanced by more than one driver. By analyzing the main beneficiaries, we grouped the drivers into three categories: users, owners, and the environment. Given the lack of consensus on the key drivers that make buildings smarter, this article contributes to filling this gap by identifying them, together with the key systems. It is also relevant for detecting the relationships between drivers and systems, and pointing out which drivers have the greatest potential to affect a particular system, keeping in mind the main beneficiary.