Management of the construction process plays a key role in ensuring that the project is delivered to the client on time, that the quality of the work is maintained and that the profit margin is kept stable (or even increased). In recent years, research efforts have focused on the automation of construction progress monitoring, which is one of the most difficult and time-consuming tasks in construction project management ^[1][2][1,2]. The purpose of monitoring is twofold: to evaluate the construction management of a project in the short term and to improve overall construction management in the long term. This includes site inspections, progress measurements and comparison with the expected performance. ^[1][3][1,3]. By comparing the planned construction progression with the actual progression, stakeholders can identify variances and delays, allowing them to take the necessary action promptly [4]. Moreover, the quality of the progress data (e.g., percentage of completion, actual start date, actual end date, and measurement date) highly depends on the surveyor’s experience and how measurements are carried out [5]. Data collected during inspections are usually manually organised and sorted and then compared with traditional and paper-based documentation. In addition to being time-consuming, this activity is also tedious [6] and, above all, disproportionately expensive [7]. In fact, it has been found that a significant proportion of a construction project manager’s time is spent measuring, recording and subsequently analysing the progress of construction works [8]. As a result of these factors, progress reports are provided too infrequently to allow for timely follow-up and effective communication between construction stakeholders. In addition, there is a lack of an effective visual interface to digitally display for a given date: built elements, elements planned but not built, and elements built but not planned.

Prompt and accurate monitoring of project performance can provide immediate insight into construction problems, and numerous attempts have been made in recent decades to automate this process ^[9][10][9,10]. Laser scanning, spherical imaging, augmented reality, barcode solutions [11] and sensing technologies are just a few examples of the proposed tracking, measurement and management techniques. Moreover, BIM-based progress monitoring has been investigated to facilitate the automated comparison of the actual state of construction with the planned state for the early detection of deviations in the construction process [12]. A first distinction between existing approaches concerns the type of construction: whether it is prefabricated (i.e., off-site construction) or on-site. In the case of off-site construction, it is relatively easy to assess construction progress, as construction sites are tidy and free of loose materials, and installed construction elements can be easily identified. On the other hand, it is more difficult to monitor multi-stage or mixed structures (e.g., load-bearing masonry, on-site reinforced concrete). Today, modern digital technologies help overcome (partially or completely) the complexity of construction progress monitoring. Several technologies have been tested in this field. They can be distinguished according to the type of sensors used, point cloud generation or image generation. Increasingly, however, these technologies are being used together to obtain accurate and detailed data.

2. Construction Progress Monitoring

The construction industry is increasingly in need of automated tools to measure construction progress, particularly approaches using remote sensing technology, as the methods typically used to measure progress are laborious and therefore time-consuming ^[13][15]. Adherence to the construction schedule of a building or infrastructure is closely linked to careful monitoring of the construction site ^[14][16]. The choice of practical data collection tools for monitoring construction progress is extremely valuable because it ensures data reliability by reducing errors. For some years, professionals and researchers have been developing various technologies for collecting site data and assessing the progress of work on construction sites. These technologies range from simple barcode scanners to sophisticated imaging tools. Barcode scanning was widely used in the 2000s, with positive results for prefabricated structures ^[15][17]. Conversely, for on-site construction, although it gave positive results due to the ease of capturing information on-site, it proved inefficient due to the time-consuming installation and maintenance of the tags ^[16][18]. The construction site is a constantly evolving and changing place, and this technology is considered too static to be sustainable. Other studies have focused on the use of imaging technology and computer vision to extrapolate useful and necessary information for construction progress. In this approach, images are used to detect and compare objects in order to assess progress. The images are taken by Unmanned Aerial Vehicles (UAVs), such as simple drones ^[17][19], sophisticated cameras or satellites. The images are then processed manually or automatically. A widely used technology for change detection is the generation of 3D point clouds. In recent years, laser scanning and photogrammetry have become the most common methods for collecting construction data ^[18][19][20,21]. El-Omari and Moselhi ^[20][22] demonstrated that these technologies can improve the accuracy and time of data collection on construction sites. Point cloud generation is possible by using static laser scanners, mobile laser scanners and photogrammetry. The operator programs and performs the survey operations with the aim of capturing the complete geometry of the artefact with maximum efficiency:

• In the first case, it means evaluating the best positions to perform the scans. The Terrestrial Laser Scanner (TLS) approach provides a local accuracy of 1 mm, but typically requires time-consuming field acquisitions and post-processing compared to the mobile alternative.
• In the second case, the mobile mapping approach requires the establishment of an optimal acquisition path. The use of mobile survey technologies results in improved flexibility and speed of data acquisition compared to the static equivalent ^[21][22][23,24]. In fact, iMMS allow for faster acquisition in large indoor areas, especially in construction sites, which are characterised by a rapidly and continuously changing environment. LiDAR-based iMMS have been developed and made efficient with the development of SLAM methods ^[23][24][25,26].
• In the third case, the photogrammetric approach requires an experienced operator capable of performing an efficient image network. In recent years, the use of photogrammetry has been increasing thanks to the diffusion of effective structure of motion commercial software that allows the use of consumer grade sensors and platforms of all types: by using UAV ^[25][27] or terrestrial images ^[26][28].

When it comes to static laser scanner-enabled applications, the most common combination is the use of LiDAR and RGB cameras. Together, they are surveying tools that can meet industrial requirements ^[27][29] and provide an accurate representation of buildings ^[28][30]. Yang ^[29][31] carried out a monotonic load measurement of an arch structure based on terrestrial laser scanning technology. Soni ^[30][32] describes the use of TLS to monitor a series of masonry arches during a major railway station refurbishment. Seo ^[31][33] used terrestrial laser scanners to evaluate the long-term behaviour of a concrete support structure by performing eight laser scans in eight different years. Subsequently, the survey results, i.e., the point clouds, obtained using the technologies described, have to be post-processed with different approaches to assess changes and can be used to perform various analyses. Wei ^[32][34] applied a segmentation approach to stereoscopic images to automatically assess the progress of construction work by placing the camera on a robot that moves around the construction site to collect the necessary information. Kavaliauska ^[33][35] proposes to survey the construction site using static and dynamic laser scanners to automatically assess the completeness of the construction by comparing the 3D survey with the BIM model. Furthermore, Alizadehsalehi ^[34][36] proposes a methodology that integrates BIM with virtual reality, augmented reality, mixed reality and extended reality ^[35][37].