Construction Progress Monitoring Research Articles

Analysing Factors Affecting Implementation of Automated Construction Progress Monitoring in Indian Construction Industry

Automated construction progress monitoring has evolved as a critical element in modern construction projects, increasing efficiency and decision-making processes. It has gained recognition as a revolutionary technology in the global construction industry. However, its successful implementation in India presents distinct problems driven by a variety of factors such as technological challenges, financial restrictions, a shortage of qualified labour, resistance to technology adoption, a high initial investment, and so on. This research explores the importance of automated construction progress monitoring, examines the factors that influence its adoption, and makes recommendations for its implementation in the Indian construction industry. By recognising these variables, construction stakeholders can better negotiate the hurdles and reap the benefits of automated monitoring technology.

Hybrid self-supervised learning-based architecture for construction progress monitoring

Automating construction progress monitoring is essential for the timely completion of projects. Computer vision-based construction progress monitoring (CV-CPM) stands out as a promising technology, leveraging 3D point clouds as inputs. Both heuristics-based and learning-based approaches have been explored for identifying building elements. Nevertheless, prevailing supervised methods require project-specific manual labeling, rendering them non-generalizable. This paper introduces a hybrid self-supervised learning architecture named ConPro-NET, which integrates heuristics with learning-based techniques for element identification from construction point clouds. The proposed approach conducts unsupervised segmentation through a region-growing-based method, followed by feature extraction using contrastive learning. Contrastive learning matches object pairs to learn their features, which are refined and augmented with handcrafted features based on local geometric and visual properties to form the hybrid feature vector. The model demonstrates an overall classification accuracy of 80.86% on the S3DIS dataset and 80.95% on a case study dataset, encompassing the classification of six object classes.

Automated progress measurement using computer vision technology in UK construction

A critical concern with the UK’s construction project progress monitoring and control techniques is their dependency on data collection, which is time consuming and unproductive and may lead to various circumstances in managing projects. However, collecting and accurately analysing information from construction sites requires the development of technologies. As key artificial intelligence (AI) technology, computer vision (CV) is a powerful tool for big data analysis that can address the aforementioned challenges. This study explores the status of CV-based construction progress monitoring (CV-CPM) adoption and the main barriers to and incentives for its adoption within the UK construction sites. In this respect, after an extensive review of the literature covering the AI technology in construction management and the concept, function and usage of CV and its integration with construction progress monitoring, including its benefits and drivers and technical challenges, a questionnaire was administered to UK construction professionals to collect their perceptions. The study results indicated that construction practitioners were relatively aware of CV-CPM but lacked competencies and skills. CV-CPM has been perceived to be relatively better than the traditional approach. Implications such as the cost of implementation, lack of expertise and resistance to change were the major challenges in CV-CPM adoption. Meanwhile, technological development, decision making and competitiveness were classified as incentives for its adoption.

Semantic segmentation-based framework for concrete pouring progress monitoring by using multiple surveillance cameras

Traditional construction progress monitoring methods face challenges in real-time monitoring of concrete pouring due to performance limitations and issues with registration and occlusion. This study proposes a framework based on multi-camera semantic fusion for monitoring the progress of concrete pouring. The construction site images are first segmented into semantic probabilities by a deep neural network. Dempster-Shafer evidence theory is then applied for semantic fusion, providing a comprehensive depiction of the progress. To reduce registration errors with the Building Information Model (BIM), a real-time perspective transformation algorithm is proposed to compensate for slight camera movements. Finally, a semantic inference method based on fully-connected Conditional Random Fields (CRFs) is employed to address occlusion by leveraging semantic context and the BIM floor plan. Comparative analysis confirmed these modules’ performance, with a remarkable reduction in relative error from 9.60% to 0.26%, enabling great potential for the continuous real-time monitoring of concrete pouring progress.

Activity-level construction progress monitoring through semantic segmentation of 3D-informed orthographic images

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.

Construction Progress Monitoring through the Integration of 4D BIM and SLAM-Based Mapping Devices

In the architecture, engineering and construction industry, site management during construction is a key phase. Scheduling activities and monitoring their progress allow any deviations from the schedule to be identified so that timely action can be taken. Until now, the monitoring phase has mainly been characterised by inspections in which the construction site manager manually collects data and produces a summary report. This proves to be a time-consuming process and is prone to errors. The authors propose an innovative construction progress monitoring method that combines BIM-based construction scheduling (4D BIM) with periodic geometric surveying using an indoor mobile mapping system (iMMS). Ten surveys were carried out on a real case study, producing point clouds to be compared with the 4D BIM, thereby comparing the as-built with the as-planned. The comparison was carried out using Sitemotion exploiting a custom class, the work breakdown structure (WBS), added to the BIM to associate each element with its scheduled construction date. The results show how the proposed method can effectively support the evaluation of construction progress, allowing the monitoring to be performed digitally and linked to the BIM. The paper details the proposed methodology, highlighting the problems encountered and suggesting adjustments for future implementation.

Exchanging Progress Information Using IFC-Based BIM for Automated Progress Monitoring

BIM has become an intrinsic tool in managing building projects due to its ability to comprehensively represent information in digital form. However, using BIM as an information exchange tool is still in its infancy, particularly with regard to construction progress monitoring beyond time schedule information. The current study focuses on the development of an automated progress monitoring framework based on an IFC-based BIM and provides an extensive methodology based on a structured task-based approach in accordance with the latest IFC4.x schema in four stages. The first stage creates the appropriate IFC entities, which are then enriched with their values in the second stage. The third stage integrates the actual progress information, which requires regular updating from the construction site. Finally, the fourth stage enables the retrieval of progress information, which is then reported in a user-friendly format along with the estimation of additional progress indicators. The proposed method successfully integrated the progress information into their IFC-based BIM models, demonstrating its practical use for monitoring construction progress. In the end, a web-based application was also developed that made use of progress information stored within the standardized hierarchy of the updated IFC-based BIM to facilitate efficient reporting.

A Systematic Literature Review on 360° Panoramic Applications in Architecture, Engineering, and Construction (AEC) Industry

While the advancement of visualization technologies—virtual-reality, augmented-reality, mixed-reality, and extended reality—has long produced opportunities to create more realistic simulated environments to provoke and study natural human behavior, recent interest in applying 360° panoramic visualizations has been increasing across several disciplines due to these technologies’ lower costs, higher presence, and greater immersive-ness. However, the variety of applications of 360° panoramas (both images and videos) is limited in the architecture, engineering, and construction (AEC) domain compared to other domains. This paper systematically presents an in-depth understanding of 360° panorama research trends and reveals the challenges and opportunities for future research in the AEC area. In particular, this systematic review analyzed eighty studies across two decades (2000-2022) to consider 360° panoramas’ application areas, methodologies, potential benefits, challenges, best practices, and future research directions for both AEC and non-AEC domains. Several prevalent application domains in AEC—namely architectural studies, construction education and training, construction visualization and progress monitoring, and cognitive analysis and human behavior in the construction industry—were identified. This paper indicates that 360° panoramas provide a higher sense of presence than conventional simulation methods (e.g., virtual reality). Moreover, pairing 360° panorama technologies with a head-mounted display significantly increases immersion when compared with other display options. Lastly, limitations of 360° panoramas, such as cybersickness and technical properties, are discussed. This paper is expected to shed light on the potential of these state-of-the-art technologies in the AEC domain, which can serve both academia and industry.

A critical appraisal of traditional methods of construction progress monitoring

PurposeThe purpose of this study is to investigate the current construction progress monitoring (CPM) process in relation to the contractual obligations, how project management teams carry out this activity in the field and why teams continue to adopt the current method. The study aims to provide a comprehensive understanding of the current monitoring process and its effectiveness, identify any shortcomings and propose recommendations for improvements that can lead to better project outcomes.Design/methodology/approachThe study conducted semi-structured interviews with 28 construction management practitioners to explore their views on contractual requirements, traditional progress monitoring practices and advanced monitoring methods. Thematic analysis was used to identify existing processes, practices and incentives for advanced monitoring.FindingsStandard construction contracts mandate current progress monitoring practices, which often rely on manual, document-centric and labor-intensive methods, leading to slow and erroneous progress reporting and project delays. Key barriers to adopting advanced tools include rigid contractual clauses, lack of incentives and the absence of reliable automated tools. A holistic automated approach that covers the entire CPM process, from planning to claim management, is needed as a viable alternative to traditional practices.Research limitations/implicationsThe study's findings can inform researchers, stakeholders and decision-makers about the existing monitoring practices and contribute to enhancing project management practices.Originality/valueThe study identified contractually mandated progress monitoring processes, traditional methods of collecting, transferring, analyzing and dispensing progress-related information and potential incentives and points of departure towards technologically advanced methods.

Robot-assisted mobile scanning for automated 3D reconstruction and point cloud semantic segmentation of building interiors

Scan-to-BIM technology can help construction stakeholders to obtain accurate BIM models for building information management and construction progress monitoring. The current practice of scan-to-BIM relies on laser scanning to capture a building's as-built condition, followed by processing scanned point clouds to generate BIM, which is laborious and time-consuming. This study presents a new robot-assisted mobile laser scanning approach using a legged robot and a solid-state LiDAR sensor, for automated 3D reconstruction and point cloud semantic segmentation. The proposed new approach involves integrating the Simultaneous Localisation and Mapping (SLAM) algorithm with robot motion control and pathfinding for 3D mapping to obtain the complete point cloud data. To ensure the completeness and density of scanned point clouds, new scanning fitness metrics and a grid-based direction-by-direction algorithm are designed for robot pathfinding. Then, an enhanced dynamic window approach is established to integrate the motion control of the legged robot with SLAM for reconstructing dense point clouds. Lastly, semantic segmentation is performed using ResPointNet++ to segment architectural and structural components. Experimental results show that the mobile scanning approach achieves a decent performance in point cloud completeness and density, with an overall mIoU of 81.75% for semantic segmentation. The ablation test demonstrates that the proposed approach is superior to the method that does not integrate the robot motion with SLAM. The proposed approach contributes to the efficient collection of high-quality point clouds for building interiors, with an aim to boost building information management.

MONITORING WORK PROGRESS OF DAM CONSTRUCTION BASED ON PHOTOGRAMMETRIC POINT CLOUDS AND BIM: PRACTICAL APPROACH TO TEACHING INDUSTRY

Abstract. Industry 4.0 encompasses a range of ideas, technologies, and procedures that impact engineering education and other fields. Building information modeling (BIM) has become a widely adopted practice in Indonesia's construction industry, prompting polytechnic universities to incorporate BIM skills and concepts into their construction engineering and management degree programs. Nowdays, more than 50 dam construction works occurred in Indonesia from 2014-2024. Monitoring progress regularly is crucial to ensure a construction project's successful completion. This can be done by visually tracking the project's advancement, which enables the identification of any changes or deviations that occur. Accurate measurement data is essential for reliable progress management, as it allows for the prediction of future project success or failure. Image-based object detection is highly beneficial for retrieving site information and monitoring construction progress. To monitor the progress of a construction project, comparisons between the building's as-built and as-planned states are necessary. BIM is employed to establish the intended condition of a building by providing information regarding its geometric characteristics and construction schedule. In this paper, we present a novel method for producing a point cloud that accurately represents the dam building site using photogrammetry. To overcome the limitation of capturing photographs from every possible angle at a construction site, we employ a combination of structure from motion and control points. This approach allows us to generate a scaled point cloud in a standardized coordinate system. The building's as-built and as-planned states are then compared using this point cloud. Structure from Motion (SfM) disparity maps are fused to create dense point clouds, which are then compared to the goal state for updating 4D BIM. This makes it possible to identify missing building components. To validate the presence of building elements, a secondary examination is conducted using the positions in front of and behind the model planes constructed as per the original blueprint. The research paper explores potential approaches and presents empirical results obtained from an actual case study.

Opportunities and Challenges of UAV Application for Monitoring the Construction Progress and Updating the Geographic Database in Urban Area of Ho Chi Minh City, Vietnam

Authorities of the Ho Chi Minh City have expressed an interest in developing application solutions of UAV (Unmanned Aerial Vehicle) that are able to monitor construction progress at different stages of any project; and to update cadastral maps for improving geospatial information for decision-making. This paper aims to describe new opportunities of using UAV in monitoring and mapping at Thu Duc district, and to address main challenges from different aspects and limitations that help engineers to improve the procedures and accuracy of UAV images processing. Some specific application cases focused on monitoring construction progress, cadastral mapping and updating the database are also introduced for understanding how UAV solutions can be applied as one tool to improve the analysis of land use change and infrastructure management in the urban area. In addition, the analysed results also shown how to develop the flight planning and the suitable Ground Control Points (GCPs) with challenges from different aspects in application solutions in capturing necessary data on monitoring construction progress and updating cadastral maps with large scale (1:200 and 1:500).

Construction progress monitoring on toll road project using photogrammetry

Unmanned Aerial Vehicle (UAV) equipped with photogrammetry has a significant impact on sustainability. Despite the advances in construction equipment and management techniques to control the complexity and dynamic nature of toll road projects, the documentation of changes which occur on site remains a prevalent issue. This research is concerned in the visualization techniques for progress monitoring that illustrates progress deviations through superimposition of four-dimensional as-planned model on surface models. The results from the digital survey into BIM Authoring Tools followed by the identification of BIM elements through surface modelling. The analysis is conducted using the parameter of the volume of land cuts and fills to quantify deviations. The data retrieved from UAV drones enriched with geospatial parameters proved to provide reliable construction records, allowing greater control of construction progress. The proposed methodology may ultimately allow project participants to adjust schedule and allocate resources in a more precise manner.

Dynamic Progress Monitoring of Masonry Construction through Mobile SLAM Mapping and As-Built Modeling

Traditional progress monitoring can be inaccurate and time-consuming, potentially causing time delay and cost overrun in construction projects. With development in technology, tools such as cameras, laser scanners, and building information modelling (BIM) have been used to overcome existing problems in the traditional approach. However, noise mitigation, extracting objects of interest from laser point clouds, and detailed progress measurement are problems that still exist. In this study a novel method of construction progress monitoring to measure the progress percentage is presented. The study integrates the simultaneous localization and mapping (SLAM) technique with as-built BIM to gather quick and accurate construction site progress information. The Hausdorff distance is utilized to extract objects of interest and filter out noise from site-scan data. As-built and as-planned BIM models are compared using Python and Dynamo, to obtain progress percentage. A case study was conducted on a residential building located in Sydney, Australia, to validate the application of the developed method. The outcome demonstrates that utilizing the SLAM technique and Hausdorff distance are effective in mitigating noise and extracting objects of interest from site-scan data, respectively. In addition, with an accuracy of 94.67 percent in estimation, the progress percentage was obtained based on material quantities. The obtained progress percentage could also be used in updating construction schedules and assisting decision-making.

Robots in Inspection and Monitoring of Buildings and Infrastructure: A Systematic Review

Regular inspection and monitoring of buildings and infrastructure, that is collectively called the built environment in this paper, is critical. The built environment includes commercial and residential buildings, roads, bridges, tunnels, and pipelines. Automation and robotics can aid in reducing errors and increasing the efficiency of inspection tasks. As a result, robotic inspection and monitoring of the built environment has become a significant research topic in recent years. This review paper presents an in-depth qualitative content analysis of 269 papers on the use of robots for the inspection and monitoring of buildings and infrastructure. The review found nine different types of robotic systems, with unmanned aerial vehicles (UAVs) being the most common, followed by unmanned ground vehicles (UGVs). The study also found five different applications of robots in inspection and monitoring, namely, maintenance inspection, construction quality inspection, construction progress monitoring, as-built modeling, and safety inspection. Common research areas investigated by researchers include autonomous navigation, knowledge extraction, motion control systems, sensing, multi-robot collaboration, safety implications, and data transmission. The findings of this study provide insight into the recent research and developments in the field of robotic inspection and monitoring of the built environment and will benefit researchers, and construction and facility managers, in developing and implementing new robotic solutions.

Automated Schedule and Cost Control Using 3D Sensing Technologies

Nowadays, many construction projects in KSA still struggle with cost overruns and delay in activities. Therefore, automatic monitoring approaches are needed in the construction progress monitoring domain (CPM) to address these concerns. Thus, this paper proposed a system integrating a BIM-planned model with site laser scans, as laser scanners showed massive potential in the CPM domain. The algorithms of the proposed system recognized 3D objects based on the intersection between models, alignment accuracy, and Lalonde features. The proposed system combined 3D object recognition technology with 5D information data into a 5D progress tracking system using earned value (EV) principles. The reason behind that is a lack of research regarding conducting a 5D assessment integrated BIM with 3D sensing technology in the CPM domain. The proposed system was verified using field data from a superstructure construction project where the object recognition indicators showed a 98% recall and 99% precision in recognizing 3D objects. The proposed system also used a color-coding system to address the condition of each element based on its recognition and scheduling state and address any occlusions while calculating the recognized objects. The results also revealed an automatically updated status of the project’s progress in terms of schedule(4D) and cost(5D). The automated results were also validated with a manual calculation, where a slight variation (1.35%) was observed between those calculations. This system demonstrates a degree of accurate progress tracking, automatically exceeding manual performance with less computational time.

A proposed model to integrate drone technology in accounting for long term contracts: a cash flow management perspictive

The accounting profession is undergoing a significant transformation due to the impact of artificial intelligence, robotic process automation, and big data. One of the latest areas of research in this field is exploring the potential use of drones for accounting and auditing tasks. However, this study takes a different approach by proposing a theoretical framework that utilizes drones for cash flow management in long-term construction projects. The proposed framework suggests that drones can be utilized as a supplementary tool to remotely conduct project site inspections and monitor construction progress. The framework addresses the percentage of completion method for recognizing revenues from long-term contracts and highlights the benefits of using drones, such as improved data quality, cost and time efficiency, increased safety during site inspections, and overall effectiveness

Effective Motion Sensors and Deep Learning Techniques for Unmanned Ground Vehicle (UGV)-Based Automated Pavement Layer Change Detection in Road Construction

As-built progress of the constructed pavement should be monitored effectively to provide prompt project control. However, current pavement construction progress monitoring practices (e.g., data collection, processing, and analysis) are typically manual, time-consuming, tedious, and error-prone. To address this, this study proposes sensors mounted using a UGV-based methodology to develop a pavement layer change classifier measuring pavement construction progress automatically. Initially, data were collected using the UGV equipped with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope, and GPS sensor in a controlled environment by constructing various scenarios of pavement layer change. Subsequently, four Long Short-Term Memory network variants (LSTMs) (LSTM, BiLSTM, CNN-LSTM, and ConvLSTM) were implemented on collected sensor data combinations for developing pavement layer change classifiers. The authors conducted the experiment to select the best sensor combinations for feature detection of the layer change classifier model. Subsequently, individual performance measures of each class with learning curves and confusion matrices were generated using sensor combination data to find out the best algorithm among all implemented algorithms. The experimental result demonstrates the (az + gx + D) sensor combination as the best feature detector with high-performance measures (accuracy, precision, recall, and F1 score). The result also confirms the ConvLSTM as the best algorithm with the highest overall accuracy of 97.88% with (az + gx + D) sensor combination data. The high-performance measures with the proposed approach confirm the feasibility of detecting pavement layer changes in real pavement construction projects. This proposed approach can potentially improve the efficiency of road construction progress measurement. This research study is a stepping stone for automated road construction progress monitoring.

A deep learning-based approach to facilitate the as-built state recognition of indoor construction works

PurposeRecognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.Design/methodology/approachThe one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.FindingsThe DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.Originality/valueThis study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

Evaluation of Photogrammetry Tools following Progress Detection of Rebar towards Sustainable Construction Processes

In the attainment of digitization and sustainable solutions under Industry 4.0, effective and economical technology like photogrammetry is gaining popularity in every field among professionals and researchers alike. In the market, various photogrammetry tools are available. These tools employ different techniques and it is hard to identify the best among them. This study is an attempt to develop a methodology for the assessment of photogrammetry tools. Overall, 37 photogrammetry tools were found via literature review and open sources, out of which 12 tools were shortlisted. The evaluation process consisted of three steps, i.e., metadata and visual inspection, comparison with the ground truth model, and comparison with the averaged-merged point cloud model. In addition, a validation test was also performed on the final sorted photogrammetry tools. This study followed a sustainable construction progress monitoring theme for rebar and covered the maximum number of photogrammetry tools for comparison by considering the most authentic evaluation and validation techniques, which make it exclusive.