Construction progress monitoring (CPM) is essential for effective project management, ensuring on-time and on-budget delivery. Traditional CPM methods often rely on manual inspection and reporting, which are time-consuming and prone to errors. This paper proposes a novel approach for automated CPM using state-of-the-art object detection algorithms. The proposed method leverages e.g. YOLOv8's real-time capabilities and high accuracy to identify and track construction elements within site images and videos. A dataset was created, consisting of various building elements and annotated with relevant objects for training and validation. The performance of the proposed approach was evaluated using standard metrics, such as precision, recall, and F1-score, demonstrating significant improvement over existing methods. The integration of Computer Vision into CPM provides stakeholders with reliable, efficient, and cost-effective means to monitor project progress, facilitating timely decision-making and ultimately contributing to the successful completion of construction projects.

We consider the bipartite boolean quadric polytope (BQP) with multiple-choice constraints and analyse its combinatorial properties. The well-studied BQP is defined as the convex hull of all quadric incidence vectors over a bipartite graph. In this work, we study the case where there is a partition on one of the two bipartite node sets such that at most one node per subset of the partition can be chosen. This polytope arises, for instance, in pooling problems with fixed proportions of the inputs at each pool. We show that it inherits many characteristics from BQP, among them a wide range of facet classes and operations which are facet preserving. Moreover, we characterize various cases in which the polytope is completely described via the relaxation-linearization inequalities. The special structure induced by the additional multiple-choice constraints also allows for new facet-preserving symmetries as well as lifting operations. Furthermore, it leads to several novel facet classes as well as extensions of these via lifting. We additionally give computationally tractable exact separation algorithms, most of which run in polynomial time. Finally, we demonstrate the strength of both the inherited and the new facet classes in computational experiments on random as well as real-world problem instances. It turns out that in many cases we can close the optimality gap almost completely via cutting planes alone, and, consequently, solution times can be reduced significantly.