01948nas a2200241   4500000000100000008004100001653003700042653002900079653004100108653002100149653001700170100001600187700001500203700002600218700001200244700001600256245014000272856007200412300001100484490000800495520118900503022001401692       2026                            d10aConstruction progress monitoring10aPercentage of completion10aConstruction activity identification10aDepth estimation10aBridge piers1 aYuyang Wang1 aElton Chen1 aMirosław Skibniewski1 aZhe Wen1 aZehao Jiang00aAutomated monitoring of percentage of completion and activity-level progress in bridge pier construction using unmanned aerial vehicles  uhttps://www.sciencedirect.com/science/article/pii/S0926580526002815  a1070400 v1883 aEffective construction progress monitoring (CPM) is essential for successful engineering projects. However, given the large number of components, wide spans, extended construction cycles, and section-by-section pouring involved in pier group construction, existing approaches struggle to quantify progress effectively. To overcome this, this paper proposes an unmanned-aerial-vehicle-based approach to evaluate both percentage of completion (POC) and construction activity at reduced cost. A depth estimation model tailored for large-scale construction scenes (LSC-Depth) is developed to address inaccuracies in weak-texture regions and those with absent structural boundaries. Construction progress is quantified by computing bridge pier height through geometric mapping. Simultaneously, construction activities are inferred from the spatial and logical relationships among site elements. The proposed approach is validated through application to an actual pier construction project. It achieves accuracies of 0.95 for POC and 0.85 for activity-level CPM. Future research shall focus on improving POC accuracy and refining activity-level granularity to enhance managerial efficiency.  a0926-5805