Development of automated weather-unaffected building construction system

Tanijiri, Hideo; Ishiguro, B.; Arai, Takashi; Yoshitake, Ryoji; Katô, Masao; Morishima, Y.; Takasaki, Nobuhiro

doi:10.1016/s0926-5805(97)00005-8

Cited by 16 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 1 lists some of the ACSs and the monitoring systems adopted in them. The components of the monitoring system involve sensors, cameras, barcodes, control room, laser, RFID and software for data collection and analysis (Sekiguchi et al, 1997;Tanijiri et al, 1997;Yamazaki and Maeda, 1998;Wakisaka et al, 2000;Ikeda and Harada, 2006). Some current ACSs lack real-time monitoring systems (Gassel;Bock and Linner, 2016b).…”

Section: Monitoring In Automated Construction Systemsmentioning

confidence: 99%

A hierarchical machine learning framework for the identification of automated construction

Harichandran¹,

Raphael²,

Mukherjee³

2021

ITcon

View full text Add to dashboard Cite

A robust monitoring system is essential for ensuring safety and reliability in automated construction. Activity recognition is one of the critical tasks in automated monitoring. Existing studies in this area have not fully exploited the potential for enhancing the performance of machine learning algorithms using domain knowledge, especially in problem formulation. This paper presents a hierarchical machine learning framework for improving the accuracy of identification of Automated Construction System (ACS) operations. The proposed identification framework arranges the operations to be identified in the form of a hierarchy and uses multiple classifiers that are organized hierarchically for separating the operation classes. It is tested on a laboratory prototype of an ACS, which follows a top-down construction method. The ACS consists of a set of lightweight and portable machinery designed to automate the construction of the structural frame of low-rise buildings . Accelerometers were deployed at critical locations on the structure. The acceleration data collected while operating the equipment were used to identify the operations through machine learning techniques. The performance of the proposed framework is compared with that of the conventional approach for equipment operation identification which involves a flat list of classes to be separated. The performance was comparable at the top level. However, the hierarchical framework outperformed the conventional one when fine levels of operations were identified. The versatility and noise tolerance of the hierarchical framework are also reported. Results demonstrate that the framework is robust, and it is feasible to identify the ACS operations precisely. Although the proposed framework is validated on a full-scale prototype of the ACS, the effects of strong ambient disturbances on actual construction sites have not been evaluated. This study will support the development of an automated monitoring system and assist the main operator to ensure safe operations. The high-level operation details collected for this purpose can also be utilised for project performance assessment and progress monitoring. The potential application of the proposed hierarchical framework in the operation recognition of conventional construction equipment is also outlined.

show abstract

Section: Monitoring In Automated Construction Systemsmentioning

confidence: 99%

A hierarchical machine learning framework for the identification of automated construction

Harichandran¹,

Raphael²,

Mukherjee³

2021

ITcon

View full text Add to dashboard Cite

show abstract

“…The Japanese construction industry has introduced fully automated construction systems (SMART, ABCS, Big Canopy, etc.) based on the concept of factory automation from the manufacturing industry (Tanijiri et al 1997;Yamazaki and Maeda 1998;Ueno 1998). In hard robotics, the focus has been moved to remote-controlled or autonomous vehicles for safe construction and maintenance work in dangerous environments, and into wearable mechanical devices to augment the capabilities of the worker (Delgado et al 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring research trends and network characteristics in construction automation and robotics based on keyword network analysis

Kim

Lee

Lim

et al. 2020

Journal of Asian Architecture and Building Engineering

View full text Add to dashboard Cite

Over the past decades, automation and robotics have been widely adopted in the construction industry as a promising solution for troubleshooting issues. This field is highly interdisciplinary and rapidly changing, and new concepts and ideas are being actively introduced in association with the existing ones. To reveal new insights, the evolution of knowledge in this field needs to be characterized by identifying important knowledge elements and research trends from published data. This study investigated research trends and network characteristics in the field of construction automation and robotics by using a keyword network analysis based on published articles. We analyzed the structure of the keyword network and identified major research topics and their relationships. Notable findings of the research characteristics are as follows. 1) Popular research themes with a set of closely connected keywords continue to be selected and contribute to the evolution of the network. 2) As an interdisciplinary research area, popular keywords that actively interact with other keywords also bridge different clusters of related research concepts. This study could provide useful insights and a better understanding of the knowledge structure and research trends as well as information on future directions in the construction automation and robotics field.

show abstract

“…Extremely bad weather that could disrupt the execution is referred to be anticipated weather or infrastructure misuse unless precautionary measures are implemented. The most common application of weather forecasting is during the construction of unique projects such as long, exposed bridges or high-rise structures(20) (21). Because these projects sometimes have substantial resources, technologies for assessing how and when on-site weather can cause planned activities to shift are more likely.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

The Use of Weather Website Data for Construction Project Decision-Making in The Short Term

Mohammed

Kadhim

Abdulrasool

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

In most work sites, it is a priority to keep the work going well and to avoid unforeseen incidents. Fluctuations in weather conditions are one of the factors affecting the continuity of work in construction projects. Indeed, for example, the temperature is important in concrete and asphalt works, and wind speed is important in lifting and high construction works. Therefore, taking the appropriate decision, starting and completing the work, is very important to maintain the quality of the project. This research aims to demonstrate the reliability of short-term decision-making through data taken from the weather site five days before the time to work. The data was collected for a month, five days before the intended day and on the same day, day and night, for different weather factors by weather location such as temperature, humidity, possibility of rain, Uv index, wind speed. By analyzing the data, it was found that there was little difference in those predictors of all the factors recorded. To conclude at the end of the study that it is possible to rely on the decision-making on the weather location in small and medium projects, but in large and sensitive projects, they need to rely on more accurate data than relying on weather location data.

show abstract

Development of automated weather-unaffected building construction system

Cited by 16 publications

References 0 publications

A hierarchical machine learning framework for the identification of automated construction

A hierarchical machine learning framework for the identification of automated construction

Exploring research trends and network characteristics in construction automation and robotics based on keyword network analysis

The Use of Weather Website Data for Construction Project Decision-Making in The Short Term

Contact Info

Product

Resources

About