Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
Data Sensing & Acquisition 2
Time:
Tuesday, 27/July/2021:
1:00pm - 3:00pm

Session Chair: Shabtai Isaac
Session Chair: Alessandro Carbonari

Subtopic: Data Acquisition, Analysis and Interpretation


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Presentations
1:00pm - 1:20pm

A PAVEMENT RATING SYSTEM BASED ON MACHINE LEARNING

Charalambos Kyriakou, Symeon E. Christodoulou

University of Cyprus

The evaluation of roadways utilizing complex contemporary datasets is currently conducted periodically because of the collection methods’ high cost. The study presents a data-driven framework on the use of a vehicle, a smartphone, an on-board diagnostic (OBD) device and machine learning for the rating of pavement surfaces. The proposed system architecture has been field-tested for the detection of pavement anomalies and the classification of five rating categories. Further, the proposed system may provide daily information on roadway pavement surface conditions, which can be used by engineers for automating the planning of pavement maintenance operations and improving public safety.



1:20pm - 1:40pm

MULTI-CRITERIA DECISION SUPPORT IN CONSTRUCTION MANAGEMENT: LIFE CYCLE-ORIENTED INVESTIGATION OF THE ECONOMIC EFFICIENCY

Lisa Lenz, Kai Christian Weist, Jan Winkels, Julian Graefenstein, Mike Gralla

Technische Universität Dortmund, Germany

The focus of this paper is an efficient data usage in order to investigate the economic efficiency of a building element. Decisions in construction management are related to the life cycle of a building in combination with numerous influencing factors there is a need for a decision support approach, which enables the user to ensure data is available and can be used efficiently to identify the best decision. To meet these challenges, this paper presents a data-based approach for combining different datasets to ensure a comprehensive base for multi-criteria decision support in construction management.



1:40pm - 2:00pm

UAS APPLICATION FOR URBAN PLANNING DEVELOPMENT IN THE DOMINICAN REPUBLIC: EMERGING COMMUNITIES AND COVID-19 SUPPORT

Hamlet David Reynoso Vanderhorst1, Subashini Suresh2, Suresh Renukappa3, David Heesom4

1University of Wolverhampton; 2University of Wolverhampton; 3University of Wolverhampton; 4University of Wolverhampton

The field of Unmanned Aerial Systems or Drones is still under development by the challenges of regulation and technology readiness for certain applications. The application of emerging technologies and robotics incites the growth of productivity on repetitive and exhaustive tasks for human and represent a rapid solution for data collection methods. The UAS presents opportunities to contribute and carry out urban planning tasks in a reduced time and risks, and appropriately supportive for COVID-19. Therefore, a case study is presented to illustrate the process of UAS data collection and conclusions drawn for delimitating urban communities.



2:00pm - 2:20pm

"BIM-to-Scan" for Scan-to-BIM: Generating Realistic Synthetic Ground Truth Point Clouds based on Industrial 3D Models

Florian Noichl, Alexander Braun, André Borrmann

Technical University of Munich, Germany

In the field of Scan-to-BIM, recent developments achieve promising results in accuracy and flexibility, leveraging tools from the field of deep learning for semantic segmentation of raw point cloud data. Those methods demand large-scale, domain-specific datasets for training. Promising ideas to fulfill this need use primitive synthetic point cloud data, which predominantly lack distinct point cloud properties, such as missing patches due to occlusions in the scene. To solve this issue, we use a specialized laser scan simulation tool from the domain of Geosciences in a toolchain that allows generating realistic ground truth data based on 3D models.



2:20pm - 2:40pm

Human Data Interaction in Sensored Sites, Challenges of the Craft Workforce Dimension

Diego Calvetti1, Pedro Mêda2, Hipólito Sousa1, Miguel Chichorro Gonçalves1

1CONSTRUCT/GEQUALTEC, Faculty of Engineering, Porto University, Portugal; 2CONSTRUCT/GEQUALTEC, Construction Institute, Faculty of Engineering, Porto University

Construction Industry (CI) is facing a new era in digitalisation. The 2020 pandemic pushed hard the sector to implement on-site sensing technologies. Craft workforce is still the primary vector of site labour performance accounting more than 50% of sector employment. A streamlined understanding of how the workforce behaves towards their data collection is more important than ever. A Human Data Interaction (HDI) vision leads to evaluate the implications of data collected in the CI glimpsing paradigms, ethics, and regulations. This work presents new use cases and frameworks to extend interactions into sensored sites focusing the craft workforce with GDPR compliance.



 
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