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The exponential growth in reality capture and Building Information Modeling (BIM)-enabled construction workflows has created a surge in computer vision–driven solutions that automatically model and compare as-built conditions against BIM, offering project teams actionable insight into construction progress and quality. Despite their significant impact, the performance of these methods heavily relies on the accuracy and completeness of the reality capture. In addition, and especially in the case of reality captures conducted with camera-equipped unmanned aerial vehicles (UAVs), operational requirements—including battery capacity and operator's line of sight (LOS)—should be carefully considered for safe flight execution. Accounting for these technical and operational requirements during reality capture planning requires expertise. In addition, it involves a significant amount of manual tweaking that does not scale well to ongoing changes due to progress on construction projects. To address these limitations, this paper presents a novel multiobjective optimization method to improve reality capture plans aiming to maximize (1) visual coverage of the monitored structure, (2) redundant observation of the structure's components in the collected frames, (3) resolution of the structure's elements in the captured data, (4) canonical camera viewpoints to the structure's topology, and (5) stability of three-dimensional (3D) reconstruction algorithms used to process the data altogether, while (6) reducing the data collection duration. The objectives are also set to meet other technical and operational requirements, particularly for camera-equipped UAVs. Furthermore, a client-server system architecture is presented to visualize, simulate, and optimize reality capture missions in a web-based 3D environment using four-dimensional (4D) BIM to indicate the as-planned expected changes. Five conducted experiments using real-world data demonstrated the method's capability to enhance the quality of user-created reality capture plans. The optimization process resulted in a 7.65% improvement in visual coverage, 30.89% enhancement in the structure's resolution, and 8.95% more stable 3D reconstruction while ensuring the flight paths meet operational requirements. [ABSTRACT FROM AUTHOR]

This paper presents new metrics and methods for evaluating the quality of reality capture plans—commonly used to operate camera‐mounted unmanned aerial vehicles (UAVs) or ground rovers—for construction progress monitoring and inspection of as‐is conditions. Using 4D building information model (BIM) or 3D reality model as a priori, these metrics provide feedback on the quality of a plan (within a few minutes), accounting for resolution, visibility, accuracy, completeness of the capture, and satisfying battery capacity and line‐of‐sight requirements. A cloud‐based system is introduced to create and optimize UAV/rover missions in the context of prior model. Results from real‐world construction data sets demonstrate that the proposed metrics offer actionable insights into the accuracy and completeness of reality capture plans. Additionally, a capture plan—with a combination of canonical and noncanonical camera views—that satisfies the introduced metrics is statistically correlated with the quality of reconstructed reality. These metrics can improve computer‐vision progress monitoring and inspection methods that rely on the construction site's appearance and geometry. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for single-worker severity level prediction from already collected site images and video clips. Onsite safety observers often assess workers' severity levels during construction activities. While risk analysis is key to improving long–term construction site safety, omnipresent monitoring is still time-consuming and costly to implement. The recent growth of visual data captured actively on construction sites has opened a new opportunity to increase the frequency of worker safety monitoring. This paper shows that a comprehensive vision-based assessment is the most informative to automatically infer worker severity level from images. Efficient computer vision models are presented to conduct this risk analysis. The method is validated on a challenging image dataset first of its kind. Specifically, the proposed method detects and evaluates the worker state from visual data, defined by (1) worker body posture, (2) the usage of personal protective equipment, (3) worker interactions with tools and materials, (4) the construction activity being performed, and (5) the presence of surrounding workplace hazards. To estimate the worker state, a multitasked recognition model is introduced that recognizes objects, activity, and keypoints from visual data simultaneously, taking 36.6% less time and 40.1% less memory while keeping comparably performances compared to a system running individual models for each subtask. Worker activity recognition is further improved with a spatio-temporal graph neural network model using recognized per-frame worker activity, detected bounding boxes of tools and materials, and estimated worker poses. Finally, severity levels are predicted by a trained classifier on a dataset of images of construction workers accompanied with ground truth severity level annotations. In the test dataset assembled from real-world projects, the severity level prediction model achieves 85.7% cross-validation accuracy in a bricklaying task and 86.6% cross-validation accuracy for a plastering task, demonstrating the potential for near real-time worker safety detection and severity assessment. [ABSTRACT FROM AUTHOR]

The architecture, engineering, and construction (AEC) industry perform thousands of scans each year. The majority of these point clouds are used for generating three-dimensional (3D) models—a process formally known as scan to building information modeling (Scan-to-BIM)—that represent the current conditions of a construction scene. Although point cloud data provide the scene's geometric information, its use presents several challenges that make the process of generating a 3D model from point cloud data time-consuming, labor-intensive, and error-prone. In order to address the mentioned challenges, this paper presents a new end-to-end deep learning method, named Scan2BIM-NET, for semantically segmenting the structural, architectural, and mechanical components present in point cloud data. It classifies beam, ceiling, column, floor, pipe, and wall elements using three main networks: two convolutional neural network (CNN) and one recurrent neural network (RNN). The method was trained and tested using 83 rooms from point cloud data representing real-world industrial and commercial buildings. The process returned an average accuracy of 86.13%, and the beam, ceiling, column, floor, pipe, and wall categories obtained an accuracy of 82.47%, 92.60%, 59.31%, 98.71%, 82.79%, and 84.46%, respectively. The experimental results showed that deep learning improves the accuracy of semantic segmentation of architectural, structural, and mechanical components. This new method has the potential of being a tool during the Scan-to-BIM process, especially for semantically segmenting underceiling areas where mechanical components are close to structural elements. [ABSTRACT FROM AUTHOR]

Current construction still suffers from significant schedule delays and budget overruns due to inefficient and retroactive project controls programs. This paper developed a visual and virtual project control system that proactively promotes team-based planning and real-time communication of progress and schedule risk. The system benefits from the growth of visual data on construction sites and the increasing maturity level of building information modeling (BIM), to map the current state of production in 3D and expose waste. By modeling production trends, reliability in the future state of production is forecasted to highlight potential delay issues in a location-driven scheme. A visual 3D interface supports collaborative decision-making to eliminate root causes of waste, enables pull flows between people and information, decentralizes work tracking, and facilitates in-process quality control and hand-overs among contractors. This system was implemented on two projects to validate the underlying algorithms. Results show that the system's functional aspects improve transparency, accountability, and traceability in project execution, and streamline communications between the field and the office. Specifically, 30% PPC increase and four weeks project completion date advance have been observed. Limitations such as resistance to workflow changes could be further improved in the future through early system introduction. [ABSTRACT FROM AUTHOR]

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]

Recent advances in reality capture technology focused on automating reality capture and devising robust computational models to convert the collected data into usable formats. However, these modern approaches are still challenged by the insufficient capacity of the resulting information to relay accurate and complete representations of the construction state due to the data's poor visual quality. In addition, the complexity of robotic path planning –especially for indoor construction– hinders automatic visual data collection due to cluttered, narrow, and dynamic construction spaces. This work targets both challenges by presenting a reinforcement learning model that optimizes indoor data collection policy for acquiring high-quality visual data using camera-equipped unmanned ground rovers. Results from three learned navigation policies show the capability of the method to provide high visual quality for the collected data. The learned policies reduced the data collection duration by 38.23% on average compared to the currently used automatic data collection strategies. The policies also provided a 31.04% average reduction in data collection distance compared to lawn-mower patterns. [Display omitted] • Reinforcement learning is used to learn a navigation policy maximizing data's visual quality and minimizing capture duration. • The optimized navigation policies are evaluated for data's visual quality and safe data collection navigation. • Divide-and-conquer algorithm reduces the optimization's computational complexity providing solutions in a timely manner. • Learnt capture plans achieve maximum visual quality in less duration than traditional collection plans. • Reinforcement learning provides ∼200% improvement in data collection duration compared to state-of-the-art NPV methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • A NLP transformer method for predicting implicit construction dependencies with 91% F1-Score. • A process for augmenting schedules with the role and flexibility of activity relationships. • A method for revising lookahead plans based on the flexibility of activity dependencies. • A Constrained Condition model for creating optimal lookahead plan revisions. Revising lookahead plans is practiced in all construction projects to meet contractual deadlines, mitigate activity delays, and disentangle process bottlenecks. We present a new machine learning-based method –which we call Implicit Logic Checker (ILC)– to learn the implicit dependency constraints and the flexibility of construction schedule relationships using a Transformer language model architecture. We then leverage relationships' flexibilities to provide a basis for creating optimal lookahead plan revisions to help mitigate the effects of unavoidable activity delays. We then deploy a Constrained Conditional (CC) model that applies learned construction planning knowledge to build revised lookahead plans and optimizes them for consistency and duration. Our ILC model is trained and tested on 35,332 manually labeled predecessor-successor relationships from eight real construction projects achieving an F1-Score of 91%. Similarly, the revised lookahead plans generated by the CC model on two case studies show reduced overall plan durations. [ABSTRACT FROM AUTHOR]

Data sharing between different organizations is critical in supporting decision making in civil infrastructure engineering projects (e.g., transportation projects). Understanding the characteristics of data-sharing-related factors in civil infrastructure engineering is crucial for the civil engineering community to identify the priorities in promoting data sharing. The ASCE Data Sensing and Analysis (DSA) Committee initiated an investigation on data-sharing barriers in civil infrastructure engineering. This work produced (1) a classification of data-collection and data-use contexts in the domain of civil infrastructure engineering; (2) a comprehensive classification of data-sharing barriers and data-sharing promotion strategies in this domain; and (3) a synthesis of the opinions about the current practices, future vision, barriers, and data-sharing promotion strategies from civil infrastructure professionals and researchers using an online survey. The survey results revealed that (1) more data-sharing activities are occurring within the organizations, such as academia, government agencies, and for-profit corporations, compared with data exchanges among these communities; (2) "if there is a cost sharing in the data sharing," is the condition that makes data providers more willing to share data with others; and (3) compared with technology improvements, the protocol of data sharing in the business domain has more potential in promoting data sharing. Meanwhile, this research identified the top five priorities among 20 data-sharing promotion strategies, which are protocol in industry and organization, data-standard regulation, data validity, data security protection, and large-scale data-sharing technology. These five priorities play critical roles in a roadmap showing how policy, business models, and technical solutions should cooperate to promote data sharing in civil infrastructure engineering. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method to predict logical relationships between any pair of schedule activities with F1-Score of 88.3%. • A method to schedule a short list of input activities based on the learned logic. • Formalization of indirect logic dependencies between schedule activities. • A method for mining indirect dependencies from an input schedule. In construction, schedule mistakes causing delays beyond substantial completion dates cost contractors expensive liquidated damages. Hence, several industry guidelines, such as the DCMA's 14 point assessment, define schedule quality and offer systematic methods for ensuring it. These guidelines list "logic" as an essential control metric, and they require planners to ensure their schedules are free of missing or wrong logical dependencies. Checking the logic requires extensive construction domain knowledge, and planners perform it entirely manually as there are no available software solutions that support it. This paper offers a novel machine learning-based solution that learns construction scheduling domain knowledge from existing records completely automatically and applies it to validate the logic in input schedules achieving an F1 score of 88.3%. Furthermore, we tailor our method to use the learned knowledge to schedule a list of unordered activities. The details of the method, experimental results, benefits, and limitations are discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • An NLP transformer for relationship prediction between look-ahead tasks and master schedule activities with 82.1% Precision. • A generative model for creating look-ahead planning task descriptions based on a master schedule activity description. • A semi-automated human-in-the-loop method for linking look-ahead tasks to master schedule activities with 76.5% Precision. • A formalized representation for matching look-ahead planning tasks to master schedule activities. In construction, master schedules and look-ahead plans are created at different times (monthly vs. weekly), by different personas (planner vs. superintendent), with different software (scheduling solution vs. spreadsheet), and at different levels of granularity (milestones vs. production details). Their full-alignment is essential for project coordination, progress updating, and payment application reviews, and its absence may lead to costly litigation. This paper presents the first attempt to automate linking look-ahead planning tasks to master-schedule activities following an NLP-based multi-stage ranking formulation. Our model employs distance-based matching for candidate generation and a Transformer architecture for final matching. 1 1 Access to the code and a sample of the dataset can be granted upon direct request from the authors. Validation results from real-world projects demonstrate that the method helps planners match look-ahead planning tasks to master schedule activities by presenting a list of top-five matches with a precision of 76.5%. We also show that the method helps superintendents create look-ahead plans from a master schedule by generating lists of tasks based on activity descriptions. [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]