(**a**) Models before generating the code (**b**) Models before generating the code

**Figure 4.** The object recognition results between models on 20 January 2021.

Three-dimensional object recognition is built mainly on the similarities between the attributes and properties in as-planned and as-actual models. Therefore, researchers used various approaches to recognize the 3D point clouds. Therefore, the case study findings and previous studies regarding object recognition results [17,19,37] are compared. The case study findings show a higher precision and recall than those presented by [17,37]. However, it is in agreement with the findings reported by [19].

#### *4.4. Schedule and Cost Control*

The proposed system generates a user interface where the calculation of the progress tracking for schedule and cost can be measured. The user interface estimates the progress of schedule and cost based on the principles of EV using the budgeted cost of work scheduled (BCWS), Budgeted cost of work performed (BCWP), and Actual cost of work performed (ACWP). Figure 5 shows the progress tracking results for the scan acquired on 25 December 2020 using the schedule and cost of concrete work in the case study. Figure 5 also shows the total cost from unrecognized elements in BCWS, BCWP, and ACWP.


**Figure 5.** The proposed system's user interface Calculation of SPI and CPI on 25 December 2020.

However, as previously illustrated, the foundations were considered the only unrecognized elements in this case study. They must be included in the schedule and cost estimation because construction activities depend on the foundations' completion. In this case study, due to the 100% completion of the unrecognized elements (the foundations), BCWS is equivalent to BCWP as presented below Cost (5D) in Figure 5.

Table 5 demonstrates the results of the 4D progress for the case study, including the costs of unrecognized elements. Using the calculated SPI, the schedule performance of the whole project at chosen scans was determined. The results showed a fast-track project where the two scans were ahead of schedule, as the SPI was larger than one in both scans. Fewer studies were conducted to update the schedule automatically [15,27,37]. These studies mainly depended on the construction schedule to show the progress. Meanwhile, this paper demonstrates the automation of an updated schedule based on the visible recognized elements and their budget unit cost to calculate the schedule performance index.


**Table 5.** Result of the earned value (SPI) to determine the project's 4D progress (Including cost from unrecognized elements).

In addition, Table 6 demonstrates the results of the 5D progress for the case study, including the costs of unrecognized elements. The cost performance of the whole project was obtained using the calculated SPI. The results showed a saving project where the two scans were under budget, as the CPI was larger than one. To the author's best knowledge, this paper is the first to address the 5D assessment in the CPM domain integrated with BIM because previous studies indicated the lack in this field, as presented in [30–32].


**Table 6.** Result of the earned value (CPI) to determine the project's 5D progress (Including cost from unrecognized elements).

#### *4.5. Comparison between Manual and Automated Calculations*

To validate the accuracy of the automated user interface, the authors compare manual and automated techniques in progress calculations. Figure 6a,b show the comparison results between the manual and automated calculations on 25 December 2020 and 20 January 2021. Firstly, on 25 December 2020, the calculations are approximately similar in BCWS, but they differ in both BCWP and ACWP calculations due to the included cost of formwork and steel fixed rebar of stairs on the second floor in manual calculations. The same issue happened on 20 January 2021, where the manual result of BCWP and ACWP was slightly higher than the automated result because the manual results included the cost of the formwork of the third-floor slab.

**Figure 6.** The results between manual and automated calculations on two selected dates: (**a**) 25 December 2020, (**b**) 20 January 2021.

The proposed system does not calculate the objects that are not fully constructed (colored in blue) but takes the occlusion objects that failed to be recognized (colored in yellow) into account in its estimation formula. The automated results differ only by 1.35% from the manual calculation. This variation comprises only the objects that are not fully constructed and the wrongly recognized objects.

Further, Table 7 compares manual and automated calculations regarding SPI and CPI, where the results showed a slight difference in SPI results between manual and automated calculations, even though it does not affect the project's status. At the same time, the CPI results showed approximate results between manual and automated calculations. The study findings above prove the validity of the proposed system. Hence, this system can be adapted to construction projects, enhancing the monitoring and controlling process as well as increasing the efficiency of schedule and cost updates with less time. This system can be developed outside the Autodesk platform, expanding the knowledge beyond one platform. Additionally, this system can be expanded to incorporate more domain knowledge (sustainability 6D, facility management 7D). Further, this system can be conducted in different types of projects, which could highlight other factors for improvement.


**Table 7.** The results of manual and automated calculation in terms of SPI and SPI.

#### **5. Conclusions**

This paper presented an automated progress-tracking system that integrates 5D information data with laser scanning using the data collected from a superstructure construction project. The proposed system algorithms were based on the intersection percentage between models, alignment accuracy, and Lalonde features. The proposed system also automatically estimates the construction progress and updates the project status in schedule and cost with less computational time. The main findings of the case study revealed that the object recognition indicators (recall and precision) achieved a remarkably decent performance of 98% and 99%, respectively. The proposed system also uses a color-coding system to address the different conditions of elements. Additionally, it also considers occlusions when calculating the recognized progress.

The proposed system also shows that the automated calculations of updated schedules and costs can improve progress estimation results compared to manual calculations, where there is a slight variation of only (1.35%) between manual and automated calculations. The reason is that the current system's estimation formula does not consider the cost of not fully constructed objects until they are completed. Thus, as future work, the current system should be evaluated in other construction buildings to declare a guideline and improvement. The authors acknowledge that the current approach has some limitations (i.e., the system is only available via the Autodesk Revit platform, and the laser scanner needs experienced labor). However, there is sufficient improvement using this approach to monitor the progress along with Earned Value principles.

**Author Contributions:** Conceptualization, A.R.E., F.K.A. and M.A.; methodology, A.R.E.; software, A.R.E.; validation, A.R.E., F.K.A. and M.A.; formal analysis, A.R.E., investigation, A.R.E., resources, A.R.E., F.K.A. and M.A.; data curation, A.R.E.; writing—original draft preparation, A.R.E.; writing—review and editing, A.R.E., F.K.A. and M.A.; visualization, A.R.E.; supervision, F.K.A. and M.A.; project administration, F.K.A. and M.A.; funding acquisition, F.K.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Researchers Supporting Project number (RSP2023R264), King Saud University, Riyadh, Saudi Arabia.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

**Acknowledgments:** The authors extend their appreciation to the Researchers Supporting Project number (RSP2023R264), King Saud University, Riyadh, Saudi Arabia for funding this work.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

List of abbreviations and acronyms used in the paper.

