Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
142,693
result(s) for
"Construction sites"
Sort by:
Construction site
Introduces vehicles commonly found at a construction site, including steamrollers, dump trucks, and excavators.
Book
Research and Application of YOLOv11-Based Object Segmentation in Intelligent Recognition at Construction Sites
With the increasing complexity of construction site environments, robust object detection and segmentation technologies are essential for enhancing intelligent monitoring and ensuring safety. This study investigates the application of YOLOv11-Seg, an advanced target segmentation technology, for intelligent recognition on construction sites. The research focuses on improving the detection and segmentation of 13 object categories, including excavators, bulldozers, cranes, workers, and other equipment. The methodology involves preparing a high-quality dataset through cleaning, annotation, and augmentation, followed by training the YOLOv11-Seg model over 351 epochs. The loss function analysis indicates stable convergence, demonstrating the model’s effective learning capabilities. The evaluation results show an mAP@0.5 average of 0.808, F1 Score(B) of 0.8212, and F1 Score(M) of 0.8382, with 81.56% of test samples achieving confidence scores above 90%. The model performs effectively in static scenarios, such as equipment detection in Xiong’an New District, and dynamic scenarios, including real-time monitoring of workers and vehicles, maintaining stable performance even at 1080P resolution. Furthermore, it demonstrates robustness under challenging conditions, including nighttime, non-construction scenes, and incomplete images. The study concludes that YOLOv11-Seg exhibits strong generalization capability and practical utility, providing a reliable foundation for enhancing safety and intelligent monitoring at construction sites. Future work may integrate edge computing and UAV technologies to support the digital transformation of construction management.
Journal Article
Job site
Following directions from the job site boss, construction workers carry out important tasks at a construction site using heavy machinery, including a bulldozer, an excavator, and a loader.
Book
Off-Site Construction Three-Echelon Supply Chain Management with Stochastic Constraints: A Modelling Approach
Off-site construction is becoming more popular as more companies recognise the benefits of shifting the construction process away from the construction site and into a controlled manufacturing environment. However, challenges associated with the component supply chain have not been fully addressed. As a result, this study proposes a model for three-echelon supply chain supply management in off-site construction with stochastic constraints. In this paper, multiple off-site factories produce various types of components and ship them to supplier warehouses to meet the needs of the construction sites. Each construction site is directly served by a supplier warehouse. The service level for each supplier warehouse is assumed to be different based on regional conditions. Because of the unpredictable nature of construction projects, demand at each construction site is stochastic, so each supplier warehouse should stock a certain number of components. The inventory control policy is reviewed regularly and is in (R, s, S) form. Two objectives are considered: minimising total cost while achieving the desired delivery time for construction sites due to their demands and balancing driver workloads during the routeing stage. A grasshopper optimisation algorithm (GOA) and an exact method are used to solve this NP-hard problem. The findings of this study contribute new theoretical and practical insights to a growing body of knowledge about supply chain management strategies in off-site construction and have implications for project planners and suppliers, policymakers, and managers, particularly in companies where an unplanned supply chain exacerbates project delays and overrun costs.
Journal Article
Tracking indoor construction progress by deep-learning-based analysis of site surveillance video
Purpose
Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.
Design/methodology/approach
A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.
Findings
The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.
Originality/value
The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.
Journal Article
Map my school
What is a map? What are maps used for? How do you read a map? Find answers to these questions as you learn about the features and uses of school, community, country and world maps--and learn how to make your own maps, too!
Book
Appraisal of stakeholders' willingness to adopt construction 4.0 technologies for construction projects
PurposeConstruction 4.0 technology has the capabilities for improving the design, management, operations and decision making of construction projects. Therefore, this study aimed at examining the willingness of construction professionals towards adopting construction 4.0 technologies.Design/methodology/approachThe study adopts a survey design, and construction professionals in South Africa are assessed using a convenience sampling technique through a structured questionnaire. The questionnaire was analysed with SPSS while statistical test like; mean score, t-test and principal component analysis was used to present the data.FindingsThe findings, from the analysis, revealed that the construction professionals are willing to adopt construction 4.0 technologies for construction project. However, the possibility of fully integrating the technologies into the construction industry is low. This is because the major technologies such as; Internet of things, robotics, human-computer interaction and cyber-physical systems that encourage smart construction site are rated as not important by the construction professionals.Practical implicationsIt is believed that the findings emanating from this study will serve as an indicator for investors that are interested in procuring construction 4.0 technologies for the construction industry.Originality/valueThis paper presents a framework for the application of construction 4.0 technologies for the construction industry. It also contributes to the development of digitalising construction industry in South Africa.
Journal Article
A Hybrid Model Based on Fuzzy AHP and Fuzzy WASPAS for Construction Site Selection
The purpose of this article is to propose a fuzzy multi-attribute perfor- mance measurement (MAPM) framework using the merits of both a novel Weighted Aggregated Sum-Product Assessment method with Fuzzy values (WASPAS-F) and Analytical Hierarchy Process (AHP). The object of this study is to select the best shopping centre construction site in Vilnius. A number of conflicting qualitative and quantitative attributes exist for evaluating alternative construction sites. Qualitative attributes are accompanied by ambiguities and vagueness. This makes fuzzy logic a more natural approach to this kind of multi-attribute decision making (MADM) prob- lems. Fuzzy AHP is applied for assigning weights of the attributes and WASPAS-F method is used to determine the most suitable alternative.
Journal Article