Construction Scene Research Articles

Perspective Transform-based Depth Estimation of Monocular Camera for Electrocution Threat Determination of Construction Machinery

Abstract. This paper discusses the application of perspective transform-based monocular camera depth estimation method in monitoring the safety distance between construction machinery and power lines. The traditional distance measurement relies on manual operation, which is inefficient and not precise enough, while the monocular depth estimation method based on deep learning can improve the accuracy, but faces high equipment cost and relies on a large amount of training data. In contrast, the depth estimation method based on perspective transformation proposed in this paper utilizes mathematical and physical principles to establish a mathematical model between the pixel distances of an image and the actual scene distances through perspective projection theory, which simplifies the hardware requirements and reduces the data dependence and improves the computational efficiency and applicability. Through experimental validation under different construction scenes and exposure conditions, the method demonstrates high accuracy and robustness, proving its practicality in construction safety management. This research provides a new technical direction and practical application possibility in the field of intelligent monitoring and 3D reconstruction.

A unified object and keypoint detection framework for Personal Protective Equipment use

Accurately detecting whether workers wear Personal Protective Equipment (PPE) in real time plays an important role in safety management. Previous studies mainly used multiple models jointly or only object detection for wearing relationship judgments. This makes it difficult to provide real-time, accurate detection of security relationships. Therefore, this paper proposes safe-wearing detection rules and a novel multi-targets and keypoints detection framework (MTKF), which is capable of accomplishing multiple classes of targets and keypoints detection simultaneously in one-stage, to get more accurate results. In order to improve the performance in the PPE and worker keypoints detection in challenging construction scenes, the detection head transformation strategy, mix group shuffle attention (MGSA) module, and the improved dual and cross-class suppression algorithm (DC-NMS) are proposed. The experimental results are implemented on one established dataset (Joint dataset) and two public datasets (SHWD and COCO), which conduct a comprehensive evaluation in multiple dimensions. Compared to the baseline model, our method improves the mAP by 2.6%–7.1%, reduces the number of parameters by at least 70%, and is able to achieve an inference speed of 155 fps.

A Petri nets-based modeling method for multi robot path planning

The construction of lunar bases is one of the core enabling technologies in current lunar exploration and development plans of various countries. However, to eliminate the constraints of high transportation costs and limited manned space technology, a new research plan is to employ multi robot teams to build lunar bases. The key to this solution is how to achieve path planning for complex tasks for multiple robots. Therefore, this article takes the exploration and collection area, lunar soil collection, and lunar soil transportation in the lunar base construction scene as complex task inputs, and studies a multi robot path planning modeling method based on Petri net model. Firstly, a Petri net model for multi robot motion was constructed. Meanwhile, linear temporal logic(LTL) language was used to describe the related tasks of lunar base construction. Finally, simulation validation was conducted in Matlab software and compared with the modeling method using switching systems. The results show that the total modeling time required for using the Petri net model is reduced by two orders of magnitude compared to the modeling time for a single task switching system model, indicating that the established Petri net multi robot model has advantages such as avoiding dimension explosion and computational efficiency.

A Reconstruction Methodology of Dynamic Construction Site Activities in 3D Digital Twin Models Based on Camera Information

Digital twin technology significantly enhances construction site management efficiency; however, dynamically reconstructing site activities presents a considerable challenge. This study introduces a methodology that leverages camera data for the 3D reconstruction of construction site activities. The methodology was initiated using 3D scanning to meticulously reconstruct the construction scene and dynamic elements, forming a model base. It further integrates deep learning algorithms to precisely identify static and dynamic elements in obstructed environments. An enhanced semi-global block-matching algorithm was then applied to derive depth information from the imagery, facilitating accurate element localization. Finally, a near-real-time projection method was introduced that utilizes the spatial relationships among elements to dynamically incorporate models into a 3D base, enabling a multi-perspective view of site activities. Validated by simulated construction site experiments, this methodology showcased an impressive reconstruction accuracy reaching up to 95%, this underscores its significant potential in enhancing the efficiency of creating a dynamic digital twin model.

Content annotation in images from outdoor construction jobsites using YOLO V8 and Swin transformer

Digital visual data, such as images and videos, are valuable sources of information for various construction engineering and management purposes. Advances in low-cost image-capturing and storing technologies, along with the emergence of artificial intelligence methods have resulted in a considerable increase in using digital imaging in construction sites. Despite these advances, these rich data sources are not typically used to their full potential because they are processed and documented subjectively, and several valuable contents could be overlooked. Semantic content analysis and annotation of the images could enhance retrieval and application of the relevant instances in large databases. This research proposes an ensemble approach to use deep learning-based object recognition, pixel-level segmentation, and text classification for medium-level (ongoing activities) and high-level (project type) annotation of still images from various outdoor construction scenes. The proposed method can annotate images with and without construction actors, i.e. equipment and workers. The experimental results have shown the potential of this approach in annotating construction activities with an 82% overall recall rate.

Sustainability Risks and Decision Making in the Construction Industry: A Case Study-Based Approach

In recent years, with the concept of sustainability put forward, more and more enterprises have considered it when making strategies and decisions. Sustainability emphasizes striking a balance between the economic, environmental, and social dimensions to ensure that current development does not jeopardize future resources and opportunities. The construction industry is not an exception. However, due to the huge energy consumption and inevitable uncertainty in the construction scenes, the sustainability concept needs to be conducted better in this industry. Current studies focused on the sustainability decision-making model choices; these studies greatly contribute to the theoretical background but could be more focused on the construction scenes and sustainability decision-making chain. This paper first revealed the risks and consequences of sustainability to point out the necessity of this research. Secondly, this paper demonstrated two case studies about sustainability decision-making. Reviewing these cases concluded a sustainability decision-making chain, which may help the construction industry reduce energy consumption and shoulder more social responsibilities.

An unsupervised low-light image enhancement method for improving V-SLAM localization in uneven low-light construction sites

Construction robots have been increasingly adopted in construction projects to improve productivity, reduce risk, and speed up work cycles. Visual Simultaneous Localization and Mapping (V-SLAM) technology is widely used in construction robots due to its lightweight weight, cost-effectiveness, and provision of semantic information. On real construction sites, lighting conditions are often challenging due to factors such as uneven lighting intensity, inadequate exposure, or robots in backlit positions (e.g. roughcast houses without artificial lighting, underground car parks), which cause images captured under these conditions to exhibit low signal-to-noise ratio, uneven lighting, color distortion, noise, and other problems. These challenges make the extraction and matching of feature points in V-SLAM difficult, resulting in significant positioning errors or the inability to generate positioning trajectories for construction robotics. Although existing methods for enhancing low-light images can certainly improve image brightness, they still cannot effectively address the localization challenges brought about by low-light construction scenes with uneven illumination and noise. To address the interference of uneven and changing lighting conditions on V-SLAM localization in construction sites, this paper proposes the Unsupervised Reflectance Retinex and Noise model (URRN-Net) to enhance low-light construction images. By using a CNN model based on the Retinex theory to decompose the illumination characteristics of images, we achieve effective brightness restoration of uneven low-light images by utilizing unsupervised loss functions. URRE-Net can reduce the root mean square localization error by >65% compared to low-light images in the ORB-SLAM3 method, and the maximum error is reduced by >71% in on-site experiments. The proposed URRE-Net can be integrated with existing V-SLAM algorithms to provide more robust localization services for low-light construction site applications such as building operations (e.g., interior wall spraying robotic) or construction management tasks (e.g., automatic tunnel inspection).

Construction personnel dress code detection based on YOLO framework

AbstractIt is important for construction personnel to observe the dress code, such as the correct wearing of safety helmets and reflective vests is conducive to protecting the workers' lives and safety of construction. A YOLO network‐based detection algorithm is proposed for the construction personnel dress code (YOLO‐CPDC). Firstly, Multi‐Head Self‐Attention (MHSA) is introduced into the backbone network to build a hybrid backbone, called Convolution MHSA Network (CMNet). The CMNet gives the model a global field of view and enhances the detection capability of the model for small and obscured targets. Secondly, an efficient and lightweight convolution module is designed. It is named Ghost Shuffle Attention‐Conv‐BN‐SiLU (GSA‐CBS) and is used in the neck network. The GSANeck network reduces the model size without affecting the performance. Finally, the SIoU is used in the loss function and Soft NMS is used for post‐processing. Experimental results on the self‐constructed dataset show that YOLO‐CPDC algorithm has higher detection accuracy than current methods. YOLO‐CPDC achieves a mAP50 of 93.6%. Compared with the YOLOv5s, the number of parameters of our model is reduced by 18% and the mAP50 is improved by 1.1%. Overall, this research effectively meets the actual demand of dress code detection in construction scenes.

Style-Enhanced Transformer for Image Captioning in Construction Scenes.

Image captioning is important for improving the intelligence of construction projects and assisting managers in mastering construction site activities. However, there are few image-captioning models for construction scenes at present, and the existing methods do not perform well in complex construction scenes. According to the characteristics of construction scenes, we label a text description dataset based on the MOCS dataset and propose a style-enhanced Transformer for image captioning in construction scenes, simply called SETCAP. Specifically, we extract the grid features using the Swin Transformer. Then, to enhance the style information, we not only use the grid features as the initial detail semantic features but also extract style information by style encoder. In addition, in the decoder, we integrate the style information into the text features. The interaction between the image semantic information and the text features is carried out to generate content-appropriate sentences word by word. Finally, we add the sentence style loss into the total loss function to make the style of generated sentences closer to the training set. The experimental results show that the proposed method achieves encouraging results on both the MSCOCO and the MOCS datasets. In particular, SETCAP outperforms state-of-the-art methods by 4.2% CIDEr scores on the MOCS dataset and 3.9% CIDEr scores on the MSCOCO dataset, respectively.

YOLO-Helmet: A novel algorithm for detecting dense small safety helmets in construction scenes

YOLO-Helmet: A novel algorithm for detecting dense small safety helmets in construction scenes

Monocular 3D object detection for construction scene analysis

AbstractThree‐dimensional (3D) object detection, that is, localizing and classifying all critical objects in a 3D space, is essential for downstream construction scene analysis tasks. However, accurate instance segmentation, few 2D object segmentation and 3D object detection data sets, high‐quality feature representations for depth estimation, and limited 3D cues from a single red‐green‐blue (RGB) image pose significant challenges to 3D object detection and severely hinder its practical applications. In response to these challenges, an improved cascade‐based network with a transformer backbone and a boundary‐patch‐refinement method is proposed to build hierarchical features and refine object boundaries, resulting in better results in 2D object detection and instance segmentation. Furthermore, a novel self‐supervised monocular depth learning method is proposed to extract better feature representations for depth estimation from construction site video data with unknown camera parameters. Additionally, a pseudo‐LiDAR point cloud method and a 3D object detection method with a density‐based clustering algorithm are proposed to detect 3D objects in a construction scene without help from 3D labels, which will serve as a good foundation for other downstream 3D tasks. Finally, the proposed model is evaluated for object instance segmentation and depth estimation on the moving objects in construction sites (MOCS) and construction scene data sets. It brings a 9.16% gain in terms of mean average precision (mAP) for object detection and a 4.92% gain in mask mAP for object instance segmentation. The average order accuracy and relative mean error for depth estimation are improved by 0.94% and 60.56%, respectively. This study aims to overcome the challenges and limitations of 3D object detection and facilitate practical applications in construction scene analysis.

Development of Mobile App to Enable Local Update on Mapping API: Construction Sites Monitoring through Digital Twin

Unmanned ground vehicles (UGVs) have emerged as a promising solution for reconnaissance missions, overcoming labor cost, frequency, and subjectivity issues associated with manual procedures. However, for dynamic environments such as construction sites, the constantly changing conditions hinder a manager from planning the UGV’s paths. For an autonomous monitoring mission, the path planning should be dealt with by having a map with the site’s most recent scene. In this study, we develop a mobile app capable of local map updates by overlaying an image on a mapping API (e.g., Google Maps) hence working as a digital twin capable of creating a dynamic representation of the updated terrain over the mapping API. UGV operators can draw a path on such an updated construction scene using a tablet PC or smartphone. Discrete GPS information (e.g., latitudinal, and longitudinal) is executed for the UGV’s controller. In the overlaying procedure, the homographic relation between the image and map is automatically computed and then projected. We successfully demonstrated the capabilities of the technique with two construction sites and a soccer field using images from the ground and satellite, respectively. The error generation is quantitatively measured and analyzed.

Knowledge‐guided digital twin modeling method of generating hierarchical scenes for a high‐speed railway

AbstractChina's railway construction is rapidly transitioning toward integrated management of “stakeholders, management elements, and management processes”. Therefore, comprehensive and whole‐process digital twin scene modeling is urgently needed for intelligent railway construction. However, the requirements of three‐dimensional scenes in different stages vary hierarchically, resulting in a lack of construction semantics and limited universality in modeling. This article proposes a knowledge‐guided digital twin modeling method of hierarchical scenes for a high‐speed railway. We first build a knowledge graph of “knowledge‐model‐data” to achieve an accurate and hierarchical description of railway scenes. We then establish a parameter‐driven modeling method that integrates knowledge guidance and primitive combination to generate a display scene and a virtual design scene automatically. Third, we propose joint linkage and model growth methods for construction action modeling, which are used to generate a virtual construction scene. Finally, in response to the hierarchical scene‐generating requirements in different stages, we conduct intelligent modeling experiments for the entire design and construction process. The knowledge graph of the hierarchical semantic description mode significantly improves the flexibility and universality of the modeling method. The proposed modeling method for the entire process contributes to the rapid representation of design data, in‐depth design, visual exploration, and dynamic optimization of the construction process. This article provides a reliable digital twin modeling solution for the entire process to improve design and construction quality.

Retracted: Construction of the 3D Reconstruction System of Building Construction Scene Based on Deep Learning

Retracted: Construction of the 3D Reconstruction System of Building Construction Scene Based on Deep Learning

Retracted: Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

Retracted: Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

Safety BIM intelligent modeling analysis of prestressed concrete continuous girder bridge construction scene

BIM intelligent modeling is used to analyze the safety of the construction scene of prestressed concrete continuous girder bridge, so as to realize the safety management of the bridge construction scene and improve the construction efficiency. The construction safety analysis system is used to analyze the binding data of construction safety, and the data is combined with the self-applicable equilibrium control and the game equilibrium control to build the construction scene safety objective function model. On this basis, combined with the control constraints of the whole life cycle, the statistical analysis regression model is used to build the scene safety analysis model based on BIM. The whole life cycle safety intelligent analysis of the construction scene is realized, and the improved particle swarm optimization algorithm is used to solve the model by adaptive differential evolution, so as to shorten the calculation time of the model. The experimental results show that the safety management performance of the proposed method is high, and the safety management evaluation grade is 285. The identification accuracy of main beam stress change is high. Under the conditions of unbalanced load, combination of unbalanced load and prestress, combination of wind load and prestress and unbalanced load, the safety analysis of upper edge stress and lower edge stress of main beam can be effectively completed, and the construction safety of prestressed concrete continuous beam bridge can be realized.

A Systematic Review of Existing Standard Form of Contract for Building Information Modeling (BIM) Public Projects in Malaysia

Building Information Modeling (BIM) has been highlighted as one of the emerging technologies following the Industrial Revolution 4.0 (IR 4.0) within the Malaysian construction scene. Its potential includes the ability to enhance a project’s collaborative aspect within a project. The application, however, remains slow in comparison to the push by the Malaysian government to be at par with developed nations. In particular, the contractual aspect does not accommodate Building Information Modeling (BIM)’s process. As a result, gaps and doubts rise for industry players as the construction industry maintains its old ways of doing things. This paper, therefore, is aimed to uncover existing standard forms of contract for BIM public projects in Malaysia. Results have found Malaysian public projects to often adopt the Public Works Department (PWD) Standard Form of Contract as a reliable form of contract adopting the design and build procurement method. Following the research outcome, a contractual framework for the adoption of BIM within the construction industry is to be devised.

A dynamic holographic modelling method of digital twin scenes for bridge construction

ABSTRACT Holographic projection technology can provide a more intuitive and efficient visualization effect for a digital twin bridge construction scene. However, pre-rendering methods in the existing research work are usually used to implement holographic visualization, which is static display. The above-mentioned methods for static display have many shortcomings, such as poor adaptability, low rendering efficiency and lack of real-time. A dynamic holographic modelling approach is proposed for the augmented visualization of digital twin scenes for bridge construction. Firstly, a dynamic segmentation algorithm with adaptive screen size was designed to high-efficiently generate holographic scenes. Secondly, a motion blur control method was designed to improve the rendering efficiency of holographic scenes according to human visual characteristics. Finally, a prototype system was developed, and the corresponding experimental analysis was completed. The experimental results show that the method proposed in this article can support adaptive screen size image segmentation and real-time generation of holographic scenes for bridge construction. The amount of scene data can be reduced to more than 30%, which significantly improves rendering efficiency and reduces glare.

Scene Graph Semantic Inference for Image and Text Matching

With the rapid development of information technology, image and text data have increased dramatically. Image and text matching techniques enable computers to understand information from both visual and text modalities and match them based on semantic content. Existing methods focus on visual and textual object co-occurrence statistics and learning coarse-level associations. However, the lack of intramodal semantic inference leads to the failure of fine-level association between modalities. Scene graphs can capture the interactions between visual and textual objects and model intramodal semantic associations, which are crucial for the understanding of scenes contained in images and text. In this article, we propose a novel scene graph semantic inference network (SGSIN) for image and text matching that effectively learns fine-level semantic information in vision and text to facilitate bridging cross-modal discrepancies. Specifically, we design two matching modules and construct scene graphs within each matching module for aggregating neighborhood information to refine the semantic representation of each object and achieve fine-level alignment of visual and textual modalities. We perform extended experiments in Flickr30K and MSCOCO and achieve state-of-the-art results, which validate the advantages of our proposed approach.

Discovery and Discrimination of Bridge Engineering Safety Issues by BIM Virtual Scene Combined with Robotic Mapping

All along, safety accidents in the field of bridge construction have continued to arise, costing society's development financial and material resources and taking away people’s personal safety, turning into an urgent need for safety managers in the field to solve the subject research. Researchers look forward to continued innovative exploration to find new ways of working safely and using them in the construction industry. On this basis, BIM virtual scenes are introduced to create a universal scene specification dataset in the field of bridge construction and a dataset of unsafe potential hazard characteristics in the field of bridge construction. Nanobots are artificial intelligence systems built to simulate biological nanomachine constructions in life activities and important biological events in life processes. In this paper, BIM virtual scenarios incorporating intelligent nanomaterial robot mapping technology are migrated to bridge engineering safety management. According to the collected construction scene images and BIM (building information model) virtual scenes, build the construction scene dataset, use intelligent robots to obtain construction scenes, and load them into the trained construction on-the-spot neural network modeling to complete the bridge construction scene identification safety discrimination to select more comprehensive and more stringent management solutions as safety management personnel management methods on-the-spot.