## 5 Application Domain
### Construction Quality Inspection  
Construction quality inspection involves checking the building elements while they are being constructed to ensure they are within tolerable limits and meet industry standards [130].The use of low-quality and changes in temperature may cause cracks in the structures [162]. Cracks in critical structural components must be inspected and documented in detail [163]. Field workers manually inspect the status and condition of the building of interest in a traditional visual inspection [130, 164]. Manual inspection requires a significant amount of manpower, which in turn increases the chances of human errors [129]. Undetected defects in the structure may affect the safety of the structure in the long run [129]. Using robots in collaboration with humans for construction quality inspection reduces labor and improves productivity and accuracy [165].  
[PERSON] et al. [130] developed a multi-robot system called AutoCIS for performing quality inspection of construction work. Quality inspection of different types of elements needs different automation approaches [130]. Some examples of quality defects found regularly in construction projects are cracks, wall cavities, surface finish defects, alignment errors, unevenness, and inclination defects [130]. Detection of each of these defects needs a different automation approach. The defect detection methods are categorized into two categories--destructive and non-destructive [130]. For robotic inspection, non-destructive methods are preferred that use different non-destructive sensors such as cameras, 3D scanners, RGB-D cameras, thermal cameras, or other sensing probes [130].  
Cracks and other surface defects can be detected by non-destructive methods. Two of the most extensively used approaches for crack-detection are edge detector-based and deep learning-based approaches [163]. While edge detection is an image processing technique that does not require large training datasets, deep learning approaches such as CNNs do [166]. [PERSON] et al. [107] used a CNN for the detection of cracks in the glass facades of high-rise towers, which is a high-risk and labor-intensive process, using a suction-mounted wall-climbing robot. More discussion on knowledge extraction methods using image processing and deep learning is presented in Section 6.2.  
UGV with LiDAR was used to detect defects in infrastructure projects such as bridge decks, which are more dangerous for humans to perform [89]. [PERSON] et al. [167] developed an encapsulated robotic system for the inspection of drilled shafts which can be used in high-water-table situations that would normally require a costly and time-consuming dewatering process for human inspection. [PERSON] et al. [101] used a cable-suspended robot for the inspection of vertical structures, such as storage tanks, ship hulls, and high-rise towers.  
Various methods for inspecting the quality of building interior components have also been developed. For example, [PERSON] et al. [168] used robots to inspect the quality of blockwork. Many defects arise during the bricklaying or blockwork activities. and inspecting and correcting them takes significant time and effort [168]. Quality inspection of indoor drywall partitions has been performed using a UAV by [PERSON] et al. [169]. [PERSON] et al. [170] developed a ground-based robot called BetoScan for the non-destructive strength-testing of concrete and reinforced concrete elements. An integrated framework for combining these techniques used by different researchers in order to develop a single robotic system capable of independently checking the quality of various elements should be developed.