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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 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]

A key underlying challenge in today's model-driven approach to engineering is how progress and level of effort are being measured and reported. Recent definitions such as level of development enable project teams to track completeness of information in building information modeling (BIM)-driven processes; yet, there are no established processes or metrics to reliably measure progress or track productivity of a model-driven engineering process as a function of the maturity of the model and/or the data that supports the engineering process. To address this gap in knowledge, this paper presents a discrete set of model maturity index (MMI) definitions to measure progress in engineering work, per modeling discipline. Using these MMI definitions, a model maturity risk index (Model MRI) toolkit was created to track maturity of an engineering process at the granularity of work breakdown structure locations. The MMI toolkit also reports risk in achieving certain MMI levels per modeling discipline. To benchmark maturity of the modeling work per project milestone or model review session and to establish workflows for tracking progress, an addendum to the modeling execution plan (ModelXP) is presented. New productivity and project controls metrics for tracking modeling work are also introduced. The developed definitions, toolkit, and addendum to ModelXP were validated through exhaustive surveys and charrette tests with project stakeholders involved in capital projects. The role of the MMI definitions and Model MRI toolkit in providing project controls insight and proactive management opportunities in real-world projects is discussed in detail. [ABSTRACT FROM AUTHOR]

Falls, struck-bys, and caught-in/betweens are among the most common types of fatal accidents on construction sites. Despite their significance, the majority of today's accident prevention programs react passively to situations in which workers or equipment enter predefined unsafe zones. To support systems that proactively prevent these accidents, this paper presents a path prediction model for workers and equipment. The model leverages the extracted video frames to predict upcoming worker and equipment motion trajectories on construction sites. Specifically, the model takes two-dimensional (2D) tracks of workers and equipment from visual data—based on computer vision methods for detection and tracking—and uses a long short-term memory (LSTM) encoder-decoder followed by a mixture density network (MDN) to predict their locations. A multihead prediction module is introduced to predict locations at different future times. The method is validated on an existing dataset, TrajNet, and a new dataset of 105 high-definition videos recorded over 30 days from a real-world construction site. On the TrajNet dataset, the proposed model significantly outperforms Social LSTM. On the new dataset, the presented model outperforms conventional time-series models and achieves average localization errors of 7.30, 12.71, and 24.22 pixels for 10, 20, and 40 future steps, respectively. The benefits and limitations of the method to worker and equipment path prediction are discussed. [ABSTRACT FROM AUTHOR]

Frequent analysis and updating the condition of traffic signs and mile markers are among the most important aspects of a highway asset management system. Today's practices mainly involve manual data collection and analysis, which have to be done for millions of miles of roads and the practice needs to be repeated regularly. While significant progress has been made on improving the data collection practice by leveraging video streams collected from car-mounted cameras, the analysis has primarily remained a manual and labor-intensive process. Automating the analysis from the collected videos is also challenging due to the interclass variability of traffic signs, expected changes in illumination, occlusion, sign position, and orientation. To address these challenges, this paper presents and evaluates the performance of three computer vision algorithms for detection and classification of traffic signs in presence of cluttered backgrounds and static and dynamic occlusions. The task particularly focuses on (1) extracting two-dimensional (2D) candidate windows from already collected video streams that potentially contain traffic signs-without making any prior assumption about their locations; (2) detecting the presence of signs in these 2D candidate windows; and (3) classifying them into warning, regulatory, stop, and yield sign categories based on their shape and color. For validation, a new comprehensive benchmark data set of over 11,000 annotated U.S. traffic sign images with a large range of pose, scale, background, illumination, and occlusion variation is introduced. Experimental results show an average accuracy of 76.20%, 89.31%, and 94.83% for the methods of (1) Haar-like features with Cascade classifiers, (2) histograms of oriented gradients (HOG) with multiple one-versus-all support vector machine (SVM) classifiers, and (3) HOG+C with the SVM classifiers, a variant of the second method with histograms of colors concatenated to HOG. The experimental results demonstrate the potential of leveraging joint representation of texture and color in HOG+C together with SVM discriminative classifiers as a viable solution for creating up-to-date and complete inventories of traffic signs for U.S. roadways. [ABSTRACT FROM AUTHOR]

The advent of affordable jobsite cameras is reshaping the way on-site construction activities are monitored. To facilitate the analysis of large collections of videos, research has focused on addressing the problem of manual workface assessment by recognizing worker and equipment activities using computer-vision algorithms. Despite the explosion of these methods, the ability to automatically recognize and understand worker and equipment activities from videos is still rather limited. The current algorithms require large-scale annotated workface assessment video data to learn models that can deal with the high degree of intraclass variability among activity categories. To address current limitations, this study proposes crowdsourcing the task of workface assessment from jobsite video streams. By introducing an intuitive web-based platform for massive marketplaces such as Amazon Mechanical Turk (AMT) and several automated methods, the intelligence of the crowd is engaged for interpreting jobsite videos. The goal is to overcome the limitations of the current practices of workface assessment and also provide significantly large empirical data sets together with their ground truth that can serve as the basis for developing video-based activity recognition methods. Six extensive experiments have shown that engaging nonexperts on AMT to annotate construction activities in jobsite videos can provide complete and detailed workface assessment results with 85% accuracy. It has been demonstrated that crowdsourcing has the potential to minimize time needed for workface assessment, provides ground truth for algorithmic developments, and most importantly allows on-site professionals to focus their time on the more important task of root-cause analysis and performance improvements. [ABSTRACT FROM AUTHOR]

Recent research efforts to improve energy modeling and diagnostics for existing buildings have focused on devising methods based on digital photogrammetry or three-dimensional (3D) laser scanning and thermal imagery. Prior research has shown that fusing actual and expected 3D spatiothermal models provide valuable information for analyzing performance gaps. Until now, these methods have primarily focused on detecting and localizing potential performance problems without analyzing their associated cost. A reliable assessment of the cost provides an opportunity to conduct cost-benefit analysis for various retrofit alternatives. It also improves current thermographic inspection which relies on visual detection and qualitative interpretation of thermal irregularities. To this end, this paper presents a new 3D thermography-based method for cost-benefit analysis of energy efficiency building envelope retrofits. The method builds on energy performance augmented reality (EPAR) modeling which generates and fuses actual and expected 3D spatiothermal models using collections of thermal and digital images. In the resulting EPAR models, first detect and calculate the building areas associated with potential thermal degradations, and then explore the as-is thermal conditions of the associated building assemblies at 3D vertex-level. Next, using the historical climatic data and the recommended R-values, the amount of the unnecessary heat transfer through the detected areas and the monetary benefits from their retrofit are estimated. Finally, the expected energy cost savings were balanced against the cost of implementing insulation retrofit alternatives. Experimental results on two real-world buildings and four hypothetical cases with different geographical conditions show potential of the proposed method in reliably estimating the return on investment from retrofitting thermal performance problems. [ABSTRACT FROM AUTHOR]

Recent research efforts to improve information management on construction sites have focused on devising mobile augmented reality methods to provide on-demand access to project information and facilitate field reporting. To track position and orientation of the users, prior research focused on radio frequency based location tracking methods, fiduciary markers, or parallel visual tracking and mapping techniques. These methods have potential in providing fast information access to field personnel, yet their application heavily depends on preinstalled infrastructure, may suffer from accumulated drift errors, and in the case of visual methods do not typically scale well. Furthermore, these methods do not provide content authoring capability in three dimensions, which is necessary for reciprocal application of augmented reality for field reporting and asynchronous personnel-to-personnel communications. To overcome these limitations, this paper presents a scalable markerless and infrastructure-independent mobile augmented reality system for commodity smartphones and tablets, with client-server architecture and cloud computing basis, to facilitate both accessing to project information and conducting field reporting in three dimensions. The method relies on structure-from-motion point clouds generated from site images and/or videos to enable image-based localization and single-image based content authoring in three dimensions. By using mobile client devices with the proposed method, onsite personnel can associate field reports with two-dimensional (2D) patches from mobile images and store that information directly in the underlying three-dimensional (3D) point cloud model. In addition, the client-server architecture of the proposed system allows an onsite access to most updated project information and field reports as they are being documented on the server. The presented architecture also handles multiple onsite information retrieval and field reporting requests simultaneously. The overall system is validated on several indoor/outdoor work areas of three real-world construction projects with varying degrees of occlusions and lighting conditions. The experimental results show the potential of the proposed method in prompt field reporting in three dimensions, minimizing the time field personnel spent on accessing project information, and communicating with others to resolve an issue/conflict on the jobsite within 1-3 min. [ABSTRACT FROM AUTHOR]

Natural disasters all too often place human lives and property at risk. Recovery efforts following a disaster can be slow and painstaking work, and potentially put responders in harm's way. A system which helps identify defects in critical building elements (e.g., concrete columns) before responders must enter a structure could save lives. In this paper we propose such a system, centered around an image-based three-dimensional (3D) reconstruction method and a new 3D crack detection algorithm. The image-based method is capable of detecting and analyzing surface damages in 3D. We also demonstrate how a robotic platform could be used to gather the set of images from which the reconstruction is created, further reducing the risk to responders. In this regard, image-based 3D reconstructions represent a convenient method of creating 3D models because most robotic platforms can carry a lightweight camera payload. Additionally, the proposed 3D crack detection algorithm also provides the advantage of being able to operate on 3D mesh models regardless of their data collection source. Our experimental results showed that the 3D crack detection algorithm performed well on several sample building elements, successfully identifying cracks, reconstructing 3D profiles, and measuring geometrical characteristics on damaged elements and not finding any cracks on intact ones. The operation and perceived benefits of the proposed method in a post-disaster situation are also discussed in detail. [ABSTRACT FROM AUTHOR]

This work provides an analysis of the applicability of two image-based three-dimensional (3D) reconstruction techniques-(1) four-dimensional augmented reality () 3D reconstruction software and (2) phototourism applications-for remote site construction project planning. Remote sites present uncertainty to construction project planning through access, material and resource availability, potential lack of technical expertise on site, and environmental considerations. Both and phototourism have the potential to minimize remote site uncertainty by using available camera technology to digitally capture existing site conditions for the development of accurate 3D modeling for construction project planning. For this study, the researchers (1) analyzed the applicability of 3D reconstruction and phototourism software applications to remote construction planning; (2) determined the appropriateness and robustness of various levels of camera technology for capturing existing conditions on remote sites; and (3) evaluated the impact of image resolution and the method of capture on 3D image-based software-all key facets to usability and access. The researchers used three cameras of various performance capabilities, i.e., a 35-mm disposable box camera, a 5.7-17.1-mm (35 mm equivalent: 37-111 mm) low-level digital camera, and a high-level digital single-lens reflex camera. The following paper outlines a methodology for testing these camera technologies and discusses their appropriateness for reducing barriers to the application of image-based reconstruction techniques for remote site construction planning. This work offers the conclusion that ordinary camera technology is an appropriate option for capturing existing conditions in remote sites (usability), while more advanced camera technology offers better total system benefits (access) of the image-based 3D reconstruction platform. offers a more robust system for modeling remote sites; however, both and phototourism have the potential to generate useful computer models using catalogs of digital images to enable better planning and execution of construction projects in remote areas. [ABSTRACT FROM AUTHOR]

Quick and reliable identification of energy performance problems in buildings is a critical step in improving their efficiency. The current practice of building diagnostics typically involves nonintrusive data collection using thermal cameras. This requires large amounts of unordered and nongeo-tagged two-dimensional (2D) imagery to be manually analyzed at a later stage, which makes the analysis time-consuming and labor-intensive. Because of the absence of a benchmark for energy performance, identification of performance problems also heavily relies on the auditor's knowledge, and consequently may lead to subjective and error-prone inspections. As a step towards rapid and objective identification of performance problems, this paper presents a new method for automated analysis and visualization of deviations between buildings' actual and simulated energy performances. The proposed method is based on the recently developed energy performance augmented reality (EPAR) environments. In the EPAR modeling method, actual and expected three-dimensional (3D) spatio-thermal models are generated and superimposed in a common 3D virtual environment. The method leverages unordered collections of thermal and digital imagery for actual energy performance modeling, in addition to computational fluid dynamics (CFD) analysis for expected energy performance simulation. Based on the EPAR models which store actual and simulated thermal values at the level of 3D points, two new algorithms are developed to facilitate identification of potential performance problems: (1) 3D thermal mesh modeling using trees and nearest-neighbor searching to automate calculation of temperature deviations, and (2) automated visualization of performance deviations using a metaphor based on traffic light colors. The proposed modeling method is validated on several interior locations of instructional and residential buildings. Empirical observations show that automated analysis using EPAR models enables performance deviations to be rapidly and accurately measured. The visualization of deviations in three dimensions enables auditors to easily identify potential performance problems and, in turn, enables auditors to focus more on other important tasks of analyzing retrofit alternatives. [ABSTRACT FROM AUTHOR]

The significant advancement in digital imaging and widespread popularity of digital cameras for capturing a comprehensive visual record of construction performance in the architecture, engineering, construction, and facility management (AEC/FM) industries have triggered an extensive growth in the rate of site photography, allowing hundreds of images to be stored for a project on a daily basis. Meanwhile, collaborative AEC technologies centering around building information models (BIMs) are becoming widely applied to support various architectural, structural, and preconstruction decision-making tasks. These models, if integrated with the as-built perspective of a construction, have great potential to extensively add value during the construction phase of a project. This paper reports recent developments from research efforts in (1) automated acquisition of as-built point-cloud models from unordered daily site photo collections and geo-registration of site images, (2) automated generation of four-dimensional (4D) as-built point-cloud models, and (3) semiautomated superimposition of the integrated as-built model over 4D (three dimensions plus time) BIMs to generate integrated 4D as-built and as-planned visualizations. The limitations and benefits of each modeling approach, the motivations for development of 4D augmented-reality (D4AR) environments for integrated visualization of as-built and as-planned models, the perceived and observed applications, and the benefits in seven case studies are discussed. Not only does a D4AR model visualize construction processes and performance deviations, it can also be used as a tool for automated and remote monitoring of progress and safety, quality control, and site layout management, and enables enhanced coordination and communication. [ABSTRACT FROM AUTHOR]

The ability to effectively communicate progress information and represent as-built and as-planned progress discrepancies are identified as key components for successful project management that allow corrective decisions to be made in a timely manner. However, current formats of reporting (e.g., textual progress reports, progress curves, and photographs) may not properly and quickly communicate project progress. Current monitoring methods also require manual data collection and extensive data extraction from different construction documents, which distract managers from the important task of decision making. Therefore, to facilitate progress monitoring, this paper proposes visualization of performance metrics that aims to represent progress deviations through superimposition of four-dimensional (4D) as-planned model over time-lapsed photographs in single and comprehensive visual imagery. As a part of the developed system, registration of the 4D model with photographs, augmenting photographs, and occlusion removal for progress images are presented. While contextual information is preserved, the as-built photographs are enhanced and augmented with 4D as-planned model in which the performance metrics are visualized. The augmented photographs provide a consistent platform for representing as-planned, as-built, and progress discrepancies information and facilitate communication and reporting processes. [ABSTRACT FROM AUTHOR]

In recent years, computer vision algorithms have shown to effectively leverage visual data from jobsites for video-based activity analysis of construction equipment. However, earthmoving operations are restricted to site work and surrounding terrain, and the presence of other structures, particularly in urban areas, limits the number of viewpoints from which operations can be recorded. These considerations lower the degree of intra-activity and interactivity category variability to which said algorithms are exposed, hindering their potential for generalizing effectively to new jobsites. Secondly, training computer vision algorithms is also typically reliant on large quantities of hand-annotated ground truth. These annotations are burdensome to obtain and can offset the cost-effectiveness incurred from automating activity analysis. The main contribution of this paper is a means of inexpensively generating synthetic data to improve the capabilities of vision-based activity analysis methods based on virtual, kinematically articulated three-dimensional (3D) models of construction equipment. The authors introduce an automated synthetic data generation method that outputs a two-dimensional (2D) pose corresponding to simulated excavator operations that vary according to camera position with respect to the excavator and activity length and behavior. The presented method is validated by training a deep learning–based method on the synthesized 2D pose sequences and testing on pose sequences corresponding to real-world excavator operations, achieving 75% precision and 71% recall. This exceeds the 66% precision and 65% recall obtained when training and testing the deep learning–based method on the real-world data via cross-validation. Limited access to reliable amounts of real-world data incentivizes using synthetically generated data for training vision-based activity analysis algorithms. [ABSTRACT FROM AUTHOR]

Accurate and efficient tracking, analysis and visualization of as-built (actual) status of buildings under construction are critical components of a successful project monitoring. Such information directly supports control decision-making and if automated, can significantly impact management of a project. This paper presents a new automated approach for recognition of physical progress based on two emerging sources of information: (1) unordered daily construction photo collections, which are currently collected at almost no cost on all construction sites; and (2) building information models (BIMs), which are increasingly turning into binding components of architecture/engineering/construction contracts. First, given a set of unordered and uncalibrated site photographs, an approach based on structure-from-motion, multiview stereo, and voxel coloring and labeling algorithms is presented that calibrates cameras, photorealistically reconstructs a dense as-built point cloud model in four dimensions (three dimensions + time), and traverses and labels the scene for occupancy. This strategy explicitly accounts for occlusions and allows input images to be taken far apart and widely distributed around the environment. An Industry Foundation Class-based (IFC-based) BIM is subsequently fused into the as-built scene by a robust registration step and is traversed and labeled for expected progress visibility. Next, a machine-learning scheme built upon a Bayesian probabilistic model is proposed that automatically detects physical progress in the presence of occlusions and demonstrates that physical progress monitoring at schedule activity level could be fully automated. Finally, the system enables the expected and reconstructed elements to be explored with an interactive, image-based, three-dimensional (3D) viewer where deviations are automatically color-coded over the IFC-based BIM. To that extent, the underlying hypotheses and algorithms for generating integrated four-dimensional (4D) as-built and as-planned models plus automated progress monitoring are presented. Experimental results are reported for challenging image data sets collected under different lighting conditions and severe occlusions from two ongoing building construction projects. This marks the presented model as being the first probabilistic model for automated progress tracking and visualization of deviations that incorporates both as-planned models and unordered daily photographs in a principled way. [ABSTRACT FROM AUTHOR]

Efficient data collection of high-quantity and low-cost highway assets such as road signs, traffic signals, light poles, and guardrails is a critical element to the operation, maintenance, and preservation of transportation infrastructure systems. Despite its importance, current practice of highway asset data collection is time-consuming, subjective, and potentially unsafe. The high volume of the data that needs to be collected can also negatively impact the quality of the analysis. To address these limitations, this paper proposes a new algorithm for semantic segmentation and recognition of highway assets using video frames collected from a car-mounted camera. The proposed set of algorithms (1) takes the captured frames and using a pipeline of structure from motion and multiview stereo reconstructs a three-dimensional (3D) point cloud model of the highway and surrounding assets; (2) using a Semantic Texton Forest classifier, each geo-registered two-dimensional (2D) video frame at the pixel-level is segmented based on shape, texture, and color of the highway assets; and finally, (3) based on the results of the 2D segmentation and a new voting scheme, each reconstructed 3D point in the cloud is also categorized for one type of asset and is color coded accordingly. The resulting augmented reality environment that integrates the color-coded point clouds with the geo-registered video frames enables a user to conduct visual walk through and query different categories of assets. Experiments were performed on a challenging video data set containing sequences filmed from a moving car on a 2.2-mi-long, two-lane highway research facility. Experimental results with an average accuracy of 76.50 and 86.75% in segmentation and pixel-level recognition of 12 types of asset categories reflect the promise of the applicability of this approach for segmentation and recognition of highway assets from image-based 3D point clouds. It also enables future algorithmic developments for 3D localization of traffic signs and other assets that are detected using the state-of-the-art vision-based methods. [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]

This paper presents a mobile workstation chariot (MWC) that enables first responders and civil engineers to undertake initial disaster reconnaissance and damage assessment operations by quickly traversing hazardous terrain and through provisioning of a necessary computing infrastructure to support real time communication with the command center. The MWC comprises a personal transporter equipped with on board communication equipment and computational processing capabilities to collect, archive, analyze, and report large quantities of data from a disaster site to provide better situation awareness of an emerging disaster scenario. In the case study presented here, the MWC uses a commercially available Segway Personal Transporter modified with a framework designed to carry a payload of information gathering and communication equipment. The MWC supports both horizontal and vertical real time data capture and transmission flow from first responders and civil engineers on the scene up to the command center by means of multilevel wireless voice and data communication infrastructures. This on board computational and visualization infrastructure allows for collecting, aggregating and deaggregating semiautomatic data, analyzing, recording, and reporting critical response and recovery information easily and quickly. [ABSTRACT FROM AUTHOR]

The aging of bridges coupled with increased vehicular traffic requires timely and accurate inspections for elevated highway structures. Recent studies have leveraged the advent of drones and computer vision to automatically conduct quick, safe, and effective inspections for elevated highway structures. However, such studies rarely offer insight or recommendations for an end-to-end integrated system that streamlines data collection, analytics, and reporting. Toward this goal, we present an end-to-end robotic bridge inspection system consisting of five tightly coupled methods to: (1) create automatic data collection missions; (2) assure visual quality of such missions; (3) reconstruct three-dimensional (3D) models of elevated structures; (4) detect and localize surface distresses in 3D; and (5) generate reports complying with highway agencies' requirements. We validate each developed method and the whole system on two representative inspection projects. Results show that our system can objectively satisfy requirements for data collection and provide up to 85.3% average precision over five defect types. We finally share lessons learned while deploying our system to 30 bridge inspection projects in the US and Japan, particularly for documenting, communicating, and following-up with bridge inspectors' recommendations. [ABSTRACT FROM AUTHOR]

Construction operations are highly energy-intensive and account for significant environmental impacts, including greenhouse gases (GHG) and other engine exhaust associated with material procurement/delivery and on-site construction activities. The opportunities for mitigating air pollutant emissions from construction operations can be captured by estimating and benchmarking during the preconstruction phase and then monitoring during the construction phase. However, a formal process model to manage the environmental aspects of on-site construction activities has yet to be clearly delineated, although recently several research efforts have appeared in relevant areas such as the development of in-use emission factors of construction equipment, methods for reliably estimating emissions at the project level, embodied carbon footprint benchmarking methods, and practical monitoring tools. Such a lack of a formal process model causes the misinterpretation of the benefits from recent research efforts in each area and hinders their implementation in practice. In this context, this paper aims to establish a framework model that could be a systematic and generic reference to the management of pollutant emissions from construction operations with state-of-the-art methods and tools. To do so, the paper identifies a generic process to be supported by research efforts in each area and presents an integrated framework based on the analysis of data connectivity between processes. An example of the indirect emission measurement is presented to demonstrate how data connectivity can be ensured between processes within the framework. [ABSTRACT FROM AUTHOR]

Numerous ecofeedback systems have been built around the proven principle that providing feedback to individuals about their energy use can lead to increased conservation through behavior change. This paper presents a novel ecofeedback system called Watt's Watts, which is designed to leverage the impacts of individual and social feedback to influence energy consumption behavior. Watt's Watts is one of the first ecofeedback systems that integrates individual-level, near-real-time energy consumption feedback within a social network environment and simultaneously collects network data that can be used to quantify social network effects on energy consumption. It was designed around two main goals: (1) to create a hardware/software integrated system that maximizes potential energy savings through behavior-based energy use reduction in residential buildings; (2) to collect data that will allow the validation of the user interface design and the examination of how social network dynamics impact energy consumption behavior. The objective of this paper is to introduce the concepts driving the design of the front and back ends of the novel Watt's Watts system to inform the design and development of future socially oriented ecofeedback systems. It explains the principles that directly informed the design of the system's interface, the methods by which the system's design is validated in numerous studies, how the back end system design enabled the robust analysis undertaken in the validation studies, and the future research avenues enabled by the development of this system. [ABSTRACT FROM AUTHOR]

Field construction can be planned, monitored, and controlled at two distinct levels: (1) the activity or schedule level; and (2) the operation or process level. Graphical three-dimensional (3D) visualization can serve as an effective communication method at both levels. Many research efforts in visualizing construction are rooted in scheduling. They typically involve linking activity-based construction schedules and 3D computer-aided design (CAD) models of facilities to describe discretely evolving construction product visualizations (often referred to as four-dimensional CAD). The focus is on communicating what components are built where and when, with the intention of studying the optimal activity sequence, spatial, and temporal interferences. The construction processes or operations actually involved in building the components are usually implied. A second approach in visualizing construction is rooted in discrete-event simulation that, in addition to visualizing evolving construction products, also concerns the visualization of the operations and processes that are performed in building them. In addition to what is built where and when, the approach communicates who builds it and how by depicting the interaction between involved machines, resources, and materials. This paper introduces the two approaches and describes the differences in concept, form, and content between activity level and operations level construction visualization. An example of a structural steel framing operation is presented to elucidate the comparison. This work was originally published in the proceedings of the 2002 IEEE Winter Simulation Conference. This paper expands on the original work by describing recent advances in both activity and operations level construction visualization. [ABSTRACT FROM AUTHOR]