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Creating consistency among project schedule data, BIM, and payment applications requires activities in a construction schedule to be mapped with the most relevant ASTM Uniformat classifications. To do so, we introduce UniformatBridge , a new transformer-based natural language processing model, that automatically labels activities in a project schedule with Uniformat classification. Our model introduces construction sequencing tokens that capture logistically-constrained predecessor and successor activities into BERT architecture. We also introduce a dataset of real-world construction project schedules with their ground-truth Uniformat classifications for validation. Experimental results using this dataset achieve F1-scores of 0.93 and 0.87 when matching unstructured schedule data to Uniformat Level 2 and 3 classifications, respectively. We share how our method unlocks development of new techniques to (1) automatically create 4D BIM, and (2) computer-vision progress monitoring to tie semantic segmentation of reality capture data based on Uniformat classes against schedule or payment application data structures, with/without BIM. • A new NLP transformer model to automatically map schedule activities to ASTM UniFormat classes. • Sequencing knowledge is embedded in the model via concatenating [pred] and [succ] tokens. • Method achieves 93% and 87% F1-Scores in mapping UniFormat II Level 2 & 3 classes with schedule activities. • Our method serves as a universal identifier enabling automated creation of 4D BIMs. • Our method streamlines mapping between schedule, cost and payment application data. [ABSTRACT FROM AUTHOR]

This paper presents VisualSiteDiary , a Vision Transformer-based image captioning model which creates human-readable captions for daily progress and work activity log, and enhances image retrieval tasks. As a model for deciphering construction photologs, VisualSiteDiary incorporates pseudo-region features, utilizes high-level knowledge in pretraining, and fine-tunes for diverse captioning styles. To validate VisualSiteDiary , a new image captioning dataset, VSD, is presented. This dataset includes many realistic yet challenging cases commonly observed in commercial building projects. Experimental results using five different metrics demonstrate that VisualSiteDiary provides superior-quality captions compared to the state-of-the-art image captioning models. Excluding the task of object recognition, the presented model also outperformed mPLUG –the state-of-the-art visual-language model– in the image retrieval task by 0.6% in precision and 0.9% in recall, respectively. Detailed discussions illustrate practical examples on how VisualSiteDiary improves the process of creating daily construction reports, paving the way for future developments in the field. [Display omitted] • New vision-language transformer model replacing costly convolutional operations. • A method for automated daily construction reporting and image retrieval. • Pseudo-region features and high-level knowledge pretraining improve the quality of captions. • New construction image-caption dataset with two different styles serving user preference. [ABSTRACT FROM AUTHOR]

Effective progress monitoring is crucial for successful construction project delivery. Visual data, comprising images and videos of construction operations, has emerged as a valuable source for gaining comprehensive insights into project status. While various vision-based methods have been developed for automated progress monitoring at the element level (e.g., columns, beams, floors, walls), challenges persist in achieving accurate schedule activity-level (e.g., formwork, reinforcement, concrete placement) progress tracking. Existing methods often struggle to report progress status beyond binary form (built/not built). To address these limitations, this research proposes a novel framework for precisely measuring the percent completion of activities associated with under-construction building elements. The framework is implemented through an Activity-level progress monitoring system (ALPMS). ALPMS takes the input of construction site images and a four-dimensional building information model (BIM) and outputs the activity-wise completion percentage required for updating project schedules. It creates as-built point clouds from images, compares them with the as-planned BIM, generates orthographic views of under-construction elements through projective transformation or neural radiance fields (NeRF), applies deep learning-based semantic segmentation for progress reasoning, and estimates completion percentages. Finally, the activity-level rich semantic information is transferred to the as-built point cloud and BIM for three-dimensional visualization of the progress status. When the framework was applied to two building construction projects, on average, the ALPMS could report the activity-wise completion percentages with <6% mean absolute error. Future research shall focus on automating the mapping of project schedule activities and activity-level progress details for timely schedule updates and prediction of project completion dates. • Activity percent complete measurement framework for outdoor and indoor construction monitoring. • Percentage completion estimation of schedule activities through semantic segmentation of orthographic view images. • Methodology for orthographic view synthesis through projective transformation and neural radiance field (NeRF). • 3D progress visualization through semantic segmentation of the as-built point cloud and color-coded BIM. • Semantic image segmentation dataset for activity level progress monitoring. [ABSTRACT FROM AUTHOR]