Search results
1 – 10 of over 7000Tirth Patel, Brian H.W. Guo, Jacobus Daniel van der Walt and Yang Zou
Current solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming…
Abstract
Purpose
Current solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming, tedious and error-prone. In this study, an automated solution proposes sensors prototype mounted unmanned ground vehicle (UGV) for data collection, an LSTM classifier for road layer detection, the integrated algorithm for as-built progress calculation and web-based as-built reporting.
Design/methodology/approach
The crux of the proposed solution, the road layer detection model, is proposed to develop from the layer change detection model and rule-based reasoning. In the beginning, data were gathered using a UGV with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope and GPS sensor in a controlled environment. The long short-term memory (LSTM) algorithm was utilised on acquired data to develop a classifier model for layer change detection, such as layer not changed, layer up and layer down.
Findings
In controlled environment experiments, the classification of road layer changes achieved 94.35% test accuracy with 14.05% loss. Subsequently, the proposed approach, including the layer detection model, as-built measurement algorithm and reporting, was successfully implemented with a real case study to test the robustness of the model and measure the as-built progress.
Research limitations/implications
The implementation of the proposed framework can allow continuous, real-time monitoring of road construction projects, eliminating the need for manual, time-consuming methods. This study will potentially help the construction industry in the real time decision-making process of construction progress monitoring and controlling action.
Originality/value
This first novel approach marks the first utilization of sensors mounted UGV for monitoring road construction progress, filling a crucial research gap in incremental and segment-wise construction monitoring and offering a solution that addresses challenges faced by Unmanned Aerial Vehicles (UAVs) and 3D reconstruction. Utilizing UGVs offers advantages like cost-effectiveness, safety and operational flexibility in no-fly zones.
Details
Keywords
The purpose of this study is to monitor the progress of construction activities in an automated way by using sensor-based technologies for tracking multiple resources that are…
Abstract
Purpose
The purpose of this study is to monitor the progress of construction activities in an automated way by using sensor-based technologies for tracking multiple resources that are used in building construction.
Design/methodology/approach
An automated on-site progress monitoring approach was proposed and a proof-of-concept prototype was developed, followed by a field experimentation study at a high-rise building construction site. The developed approach was used to integrate sensor data collected from multiple resources used in different steps of an activity. It incorporated the domain-specific heuristics that were related to the site layout conditions and method of activity.
Findings
The prototype estimated the overall progress with 95% accuracy. More accurate and up-to-date progress measurement was achieved compared to the manual approach, and the need for visual inspections and manual data collection from the field was eliminated. Overall, the field experiments demonstrated that low-cost implementation is possible, if readily available or embedded sensors on equipment are used.
Originality/value
Previous studies either monitored one particular piece of equipment or the developed approaches were only applicable to limited activity types. This study demonstrated that it is technically feasible to determine progress at the site by fusing sensor data that are collected from multiple resources during the construction of building superstructure. The rule-based reasoning algorithms, which were developed based on a typical work practice of cranes and hoists, can be adapted to other activities that involve transferring bulk materials and use cranes and/or hoists for material handling.
Details
Keywords
Yuyu Hao, Shugang Li and Tianjun Zhang
This paper aims to propose a deployment optimization and efficient synchronous acquisition method for compressive stress sensors used by stress distribution law research based on…
Abstract
Purpose
This paper aims to propose a deployment optimization and efficient synchronous acquisition method for compressive stress sensors used by stress distribution law research based on the genetic algorithm and numerical simulations. The authors established a new method of collecting the mining compressive stress-strain distribution data to address the problem of the number of sensors and to optimize the sensor locations in physical similarity simulations to improve the efficiency and accuracy of data collection.
Design/methodology/approach
First, numerical simulations were used to obtain the compressive stress distribution curve under specific mining conditions. Second, by comparing the mean square error between a fitted curve and simulation data for different numbers of sensors, a genetic algorithm was used to optimize the three-dimensional (3D) spatial deployment of sensors. Third, the authors designed an efficient synchronous acquisition module to allow distributed sensors to achieve synchronous and efficient acquisition of hundreds of data points through a built-in on-board database and a synchronous sampling communication structure.
Findings
The sensor deployment scheme was established through the genetic algorithm, A synchronous and selective data acquisition method was established for reduced the amount of sensor data required under synchronous acquisition and improved the system acquisition efficiency. The authors obtained a 3D compressive stress distribution when the advancement was 200 m on a large-scale 3D physical similarity simulation platform.
Originality/value
The proposed method provides a new optimization method for sensor deployment in physical similarity simulations, which improves the efficiency and accuracy of system data acquisition, providing accurate acquisition data for experimental data analysis.
Details
Keywords
Princy Randhawa, Vijay Shanthagiri, Ajay Kumar and Vinod Yadav
The paper aims to develop a novel method for the classification of different physical activities of a human being, using fabric sensors. This method focuses mainly on classifying…
Abstract
Purpose
The paper aims to develop a novel method for the classification of different physical activities of a human being, using fabric sensors. This method focuses mainly on classifying the physical activity between normal action and violent attack on a victim and verifies its validity.
Design/methodology/approach
The system is realized as a protective jacket that can be worn by the subject. Stretch sensors, pressure sensors and a 9 degree of freedom accelerometer are strategically woven on the jacket. The jacket has an internal bus system made of conductive fabric that connects the sensors to the Flora chip, which acts as the data acquisition unit for the data generated. Different activities such as still, standing up, walking, twist-jump-turn, dancing and violent action are performed. The jacket in this study is worn by a healthy subject. The main phases which describe the activity recognition method undertaken in this study are the placement of sensors, pre-processing of data and deploying machine learning models for classification.
Findings
The effectiveness of the method was validated in a controlled environment. Certain challenges are also faced in building the experimental setup for the collection of data from the hardware. The most tedious challenge is to collect the data without noise and error, created by voltage fluctuations when stretched. The results show that the support vector machine classifier can classify different activities and is able to differentiate normal action and violent attacks with an accuracy of 98.8%, which is superior to other methods and algorithms.
Practical implications
This study leads to an understanding of human physical movement under violent activity. The results show that data compared with normal physical motion, which includes even a form of dance is quite different from the data collected during violent physical motion. This jacket construction with woven sensors can capture every dimension of the physical motion adding features to the data on which the machine learning model will be built.
Originality/value
Unlike other studies, where sensors are placed on isolated parts of the body, in this study, the fabric sensors are woven into the fabric itself to collect the data and to achieve maximum accuracy instead of using isolated wearable sensors. This method, together with a fabric pressure and stretch sensors, can provide key data and accurate feedback information when the victim is being attacked or is in a normal state of action.
Details
Keywords
Ali Rashidi, George Lukic Woon, Miyami Dasandara, Mohsen Bazghaleh and Pooria Pasbakhsh
The construction industry remains one of the most hazardous industries worldwide, with a higher number of fatalities and injuries each year. The safety and well-being of workers…
Abstract
Purpose
The construction industry remains one of the most hazardous industries worldwide, with a higher number of fatalities and injuries each year. The safety and well-being of workers at a job site are paramount as they face both immediate and long-term risks such as falls and musculoskeletal disorders. To mitigate these dangers, sensor-based technologies have emerged as a crucial tool to promote the safety and well-being of workers on site. The implementation of real-time sensor data-driven monitoring tools can greatly benefit the construction industry by enabling the early identification and prevention of potential construction accidents. This study aims to explore the innovative method of prototype development regarding a safety monitoring system in the form of smart personal protective equipment (PPE) by taking advantage of the recent advances in wearable technology and cloud computing.
Design/methodology/approach
The proposed smart construction safety system has been meticulously crafted to seamlessly integrate with conventional safety gear, such as gloves and vests, to continuously monitor construction sites for potential hazards. This state-of-the-art system is primarily geared towards mitigating musculoskeletal disorders and preventing workers from inadvertently entering high-risk zones where falls or exposure to extreme temperatures could occur. The wearables were introduced through the proposed system in a non-intrusive manner where the safety vest and gloves were chosen as the base for the PPE as almost every construction worker would be required to wear them on site. Sensors were integrated into the PPE, and a smartphone application which is called SOTER was developed to view and interact with collected data. This study discusses the method and process of smart PPE system design and development process in software and hardware aspects.
Findings
This research study posits a smart system for PPE that utilises real-time sensor data collection to improve worksite safety and promote worker well-being. The study outlines the development process of a prototype that records crucial real-time data such as worker location, altitude, temperature and hand pressure while handling various construction objects. The collected data are automatically uploaded to a cloud service, allowing supervisors to monitor it through a user-friendly smartphone application. The worker tracking ability with the smart PPE can help to alleviate the identified issues by functioning as an active warning system to the construction safety management team. It is steadily evident that the proposed smart PPE system can be utilised by the respective industry practitioners to ensure the workers' safety and well-being at construction sites through monitoring of the workers with real-time sensor data.
Originality/value
The proposed smart PPE system assists in reducing the safety risks posed by hazardous environments as well as preventing a certain degree of musculoskeletal problems for workers. Ultimately, the current study unveils that the construction industry can utilise cloud computing services in conjunction with smart PPE to take advantage of the recent advances in novel technological avenues and bring construction safety management to a new level. The study significantly contributes to the prevailing knowledge of construction safety management in terms of applying sensor-based technologies in upskilling construction workers' safety in terms of real-time safety monitoring and safety knowledge sharing.
Details
Keywords
Anne Tolman and Tommi Parkkila
The purpose of this paper is to describe how healthy performance of facilities can be monitored and performance data delivered as information flow according to specific user…
Abstract
Purpose
The purpose of this paper is to describe how healthy performance of facilities can be monitored and performance data delivered as information flow according to specific user groups' needs.
Design/methodology/approach
The context of performance in facilities is described, and a tool for the collection of performance data and communicating the data as relevant information for the facility management (FM) and other stake holders is developed.
Findings
Various user groups are utilizing the same performance data to ensure optimal and healthy conditions. The integration of performance data to deliver meaningful and exploitable results for each user requires collection of the relevant data, compilation of data into information, and delivery of user specific information to the correct instance. A performance sensing system with data management was developed into a FM tool for this purpose.
Practical implications
FM is enabled to real time decision making by sensor‐based performance monitoring.
Originality/value
The results are generic and FM tools may be built on this basis for the specific information and display needs of various FM professionals and other stakeholders.
Details
Keywords
Chiara Tagliaro, Yaoyi Zhou and Ying Hua
Workplace space utilization data reveals patterns of space usage, the occupants’ presence and mobility within the office building. Nowadays, emerging technology such as smart…
Abstract
Purpose
Workplace space utilization data reveals patterns of space usage, the occupants’ presence and mobility within the office building. Nowadays, emerging technology such as smart sensors and devices can revolutionize the measurement of space utilization data, which is originally dominated by human observers with paper and pencil. However, these novel instruments are often used in an old fashion, which restricts the exploitation of their full potential. This study aims to shed new light on the benefits and limits of using smart technology in measuring space utilization data and discusses the challenges and opportunities in analyzing the data measured by smart sensors.
Design/methodology/approach
First, the literature regarding common methods and previous studies about office space utilization measurement was reviewed. Then, a data set consisting of space utilization data collected through Passive Infra-Red sensors for 35 meeting rooms in a bank building was carefully evaluated. Finally, the space utilization results based on methods calculated in two different granularities were compared.
Findings
The number of occupied hours calculated at an hour level was 1.32-hour larger than that calculated at a minute level. As both results show the concept of space utilization, which was the amount of time that the space was occupied, this paper revealed a gap between the two space utilization calculation methods and further discussed the issues and challenges for future space utilization data analysis and benchmarking.
Originality/value
To the best of the authors’ knowledge, this is the first study critically addressing office space utilization issues by comparing calculation methods in different granularity.
Details
Keywords
Junying Chen, Zhanshe Guo, Fuqiang Zhou, Jiangwen Wan and Donghao Wang
As the limited energy of wireless sensor networks (WSNs), energy-efficient data-gathering algorithms are required. This paper proposes a compressive data-gathering algorithm based…
Abstract
Purpose
As the limited energy of wireless sensor networks (WSNs), energy-efficient data-gathering algorithms are required. This paper proposes a compressive data-gathering algorithm based on double sparse structure dictionary learning (DSSDL). The purpose of this paper is to reduce the energy consumption of WSNs.
Design/methodology/approach
The historical data is used to construct a sparse representation base. In the dictionary-learning stage, the sparse representation matrix is decomposed into the product of double sparse matrices. Then, in the update stage of the dictionary, the sparse representation matrix is orthogonalized and unitized. The finally obtained double sparse structure dictionary is applied to the compressive data gathering in WSNs.
Findings
The dictionary obtained by the proposed algorithm has better sparse representation ability. The experimental results show that, the sparse representation error can be reduced by at least 3.6% compared with other dictionaries. In addition, the better sparse representation ability makes the WSNs achieve less measurement times under the same accuracy of data gathering, which means more energy saving. According to the results of simulation, the proposed algorithm can reduce the energy consumption by at least 2.7% compared with other compressive data-gathering methods under the same data-gathering accuracy.
Originality/value
In this paper, the double sparse structure dictionary is introduced into the compressive data-gathering algorithm in WSNs. The experimental results indicate that the proposed algorithm has good performance on energy consumption and sparse representation.
Details
Keywords
Emil L. Jacobsen, Alex Solberg, Olga Golovina and Jochen Teizer
Accidents resulting from poorly planned or setup work environments are a major concern within the construction industry. While traditional education and training of personnel…
Abstract
Purpose
Accidents resulting from poorly planned or setup work environments are a major concern within the construction industry. While traditional education and training of personnel offer well-known approaches for establishing safe work practices, serious games in virtual reality (VR) are increasingly being used as a complementary approach for active learning experiences. By taking full advantage of data collection and the interactions possible in the virtual environment, the education and training of construction personnel improves by using non-biased feedback and immersion.
Design/methodology/approach
This research presents a framework for the generation and automated assessment of VR data. The proposed approach is tested and evaluated in a virtual work environment consisting of multiple hazards. VR requires expensive hardware, technical knowledge and user acceptance to run the games effectively. An effort has been made to transfer the advantages VR gives to a physical setup. This is done using a light detection and ranging sensing system, which collects similar data and enables the same learning experiences.
Findings
Encouraging results on the participants’ experiences are presented and discussed based on actual needs in the Danish construction industry. An outlook presents future avenues towards enhancing existing learning methods.
Practical implications
The proposed method will help develop active learning environments, which could lead to safer construction work stations in the future, either through VR or physical simulations.
Originality/value
The utilization of run-time data collection and automatic analysis allows for better personalized feedback in the construction safety training. Furthermore, this study investigates the possibility of transferring the benefits of this system to a physical setup that is easier to use on construction sites without investing in a full VR setup.
Details
Keywords
Catherine D’Ignazio, Eric Gordon and Elizabeth Christoforetti
The ability to gather, store, and make meaning from large amounts of sensor data is becoming a technological and financial reality for cities. Many of these initiatives are…
Abstract
The ability to gather, store, and make meaning from large amounts of sensor data is becoming a technological and financial reality for cities. Many of these initiatives are happening through deals brokered between vendors, developers, and cities. They are made manifest in the environment as infrastructure – invisible to citizens and communities. We assert that in order to have community-centered smart cities, we need to transform sensor data collection and usage from invisible infrastructure into visible and legible interface. In this chapter, we compare two different urban sensing initiatives and examine the methods used for feedback between sensors and people. We question how value gets produced and communicated to citizens in urban sensing projects and what kind of oversight and ethical considerations are necessary. Finally, we make a case for “seamful” interfaces between communities, sensors, and cities that reveal their inner workings for the purposes of civic pedagogy and dialogue.
Details