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Construction Information Technology Laboratory

 

Current Projects

FUTUREROADS: FUTURE ROADS FELLOWSHIPS

September 2021 – August 2026

European Commission, H2020, MSCA £5,226,406

 

BIM2TWIN: OPTIMAL CONSTRUCTION MANAGEMENT & PRODUCTION CONTROL

November 2020 – March 2024

European Commission, H2020, £5,217,378

BIM2TWIN aims to build a Digital Building Twin (DBT) platform for construction management that implements lean principles to reduce operational waste of all kinds, shortening schedules, reducing costs, enhancing quality and safety and reducing carbon footprint. BIM2TWIN proposes a comprehensive, holistic approach. It consists of a (DBT) platform that provides full situational awareness and an extensible set of construction management applications. It supports a closed loop Plan-Do-Check-Act mode of construction. Its key features are:

  1. Grounded conceptual analysis of data, information and knowledge in the context of DBTs, which underpins a robust system architecture.
  2. A common platform for data acquisition and complex event processing to interpret multiple monitored data streams from construction site and supply chain to establish real-time project status in a Project Status Model (PSM).
  3. Exposure of the PSM to a suite of construction management applications through an easily accessible application programming interface (API) and directly to users through a visual information dashboard.
  4. Applications include monitoring of schedule, quantities & budget, quality, safety, and environmental impact.
  5. PSM representation based on property graph semantically linked to the Building Information Model (BIM) and all project management data. The property graph enables flexible, scalable storage of raw monitoring data in different formats, as well as storage of interpreted information. It enables smooth transition from construction to operation.

 

CLOUD-BASED BUILDING INFORMATION MODELLING

March 2020 – February 2024

European Union's Horizon 2020 research and innovation programme, £3,430,782

CBIM is a European Training Network in the area of Cloud-based Building Information Modelling. CBIM brings together five leading universities, two software companies and a research institute from six countries, to provide PhD training through state-of-the-art research.

Building Information Modelling (BIM) as a product and process enables stakeholders across the built environment sector to create digital versions of real world assets (such as buildings, bridges and tunnels). The digital versions are commonly called 'digital twins'. When placed on the cloud, the digital twins can serve as a resilient and integrated repository of all asset data throughout their life-cycle. Such a repository is a key enabler in this sector of all upcoming IT waves, such as cloud computing, data analytics, participatory sensing, and smart infrastructure. The potential benefits have attracted interest from a wide array of end-users whose interests span from early design phases to operation and asset management, and from roads and bridges to industrial off-shore facilities. This has led to aggressive market penetration in the last decade. However, the full potential of BIM is currently exploited only in a fairly narrow range of applications. This is mainly due to the lack of trained scientific personnel capable of understanding the value of BIM and creating the link between digital twins and possible applications.
The ambition of CBIM is therefore to educate researchers in the development of a set of novel and disruptive BIM technologies that will automate the generation and enrichment of digital twins, improve the management, security and resilience of BIM-enabled processes, and boost the industrial uptake of BIM across sectors and disciplines by training these researchers to valorise and exploit their work. This new generation of researchers can play a key role in the widespread implementation of BIM products and processes dedicated to digitising our built infrastructure and managing our assets better to yield massive gains in sustainability, productivity and safety.

Researchers: Zhiqi Hu, Jialei Ding and Viktor Drobnyi

 

CENTRE FOR DOCTORAL TRAINING IN FUTURE INFRASTRUCTURE AND BUILT ENVIRONMENT-2 (FIBE2)

Oct 2019 - Mar 2027

Eng. & Physical Sciences Res. Council, UK, £8,747,084

The FIBE2 CDT tackles the strategically important theme of infrastructure resilience in the context of five categories of threats (and associated opportunities):

(i) Infrastructure resilience against technological uncertainty

(ii) Infrastructure resilience against environmental causes:

(iii) Infrastructure resilience in a world of economic and political change

(iv) Infrastructure resilience to support urbanisation and demographic change

(v) Infrastructure resilience in a changing society and culture

Researcher: KM White

 

HANS FISCHER SENIOR FELLOWSHIP: DIGITAL TWIN FOR THE BUILT ENVIRONMENT

Jun 2019 - Sep 2021

TUM - Institute for Advance study, £172,444

The award was made in recognition of Dr Brilakis’ contributions to the area of “Digital Twin for the Built Environment”. His Construction IT group has made pioneering scientific accomplishments in “twinning” infrastructure scenes, i.e. extracting a rich digital copy (digital twin) of real world infrastructure scenes, such as a) buildings and industrial plants, b) bridges, c) tunnels, d) roads and e) railways, such that the digital twin can be used for managing, maintaining and retrofitting the modelled assets.

 

AUTOMATED BIM GENERATION OF EXISTING AND UNDER CONSTRUCTION RAILWAYS

October 2017 – September 2021

Bentley Systems UK Ltd. Grant, £93,574

The process is of as-is infrastructure modelling involves the generation of infrastructure objects and their relationships from low-level point cloud datasets and the design BIM file.This research is aimed at creating a viable approach to automate the generation of as-is geometric railway Building Information Modelling (BIM).The key novel idea that makes this possible is that railways follow a set of engineering design assumptions which can be used as guides for segmentation by Markov Chain Monte Carlo (MCMC) methods, region growing, or octrees.The assumptions can be formulated as rules-sets can be used to recursively segment the data multiple times down to the individual component level through deep learning methods, such as recurrent neutral networks and conditional random fields (CRF). Template priors and fitting methods could then be used to replace segmented high-level primitives with actual objects and their relationships.

Researcher: Mahendrini Fernando.

 

INTERACTIVE POINT CLOUD AND IMAGE DATA GENERATION IN INFRASTRUCTURE SCENES

October 2017 – September 2021

Trimble Europe BV Grant, Laing O'Rourke, BP International Ltd, Topcon GB Ltd, GeoSLAM Ltd, £160,000

All construction trades and inspectors need spatial and visual data to operate. However, extracting up to hundreds or thousands of measurements in a single work shift and creating meaningful registered data sets translates to a sizable portion of work time dedicated to measuring and processing instead of direct work. The research is mainly focused on increasing the efficiency of geometry and visual data collection. The objective of this PhD studentship is to devise a system that tackles the challenges both in data collection and post-processing. The possibility of this work will be investigated by combining a hand held laser scanner with high-resolution DSLR cameras. The output of this research will be combined, auto-registered PCD and ID data set in real time for outdoor, large scale IS. Construction operatives and inspectors could benefit significantly from laser-scanner-quality data in terms of resolution and accuracy, with the mobility and reachability of hand held devices and the ability to operate outdoors reliably and from long distances.

Researcher: Maciej Trzeciak

 

Past Projects

TOP-DOWN-AS-IS MODELLING OF INDUSTRIAL FACILITIES

October 2016 - September 2020

EPSRC & AVEVA Group Plc Grant, £102,536

This research will explore ways to detect existing building objects in spatial and visual data for the purpose of automating the generation of as-built geometric models of industrial facilities. The research will address both construction engineering and computer vision issues.

Researcher: Eva Agapaki

 

AUTOMATIC CONSTRUCTION MONITORING THROUGH SEMANTIC INFORMATION MODELLING

April 2017 – March 2020

Discovery Grants, External PI: Prof. Xiangyu Wang, Curtin University £204,016

This project aims to develop innovative computational algorithms and methods for automatic as-built construction monitoring through semantics-based Building Information Modelling (BIM). The project will tackle scientific bottleneck of modelling processes with advanced computational methods so as to generate new know-how and provide efficient and accurate solutions for as-built construction monitoring.

The implementation of the as-build modelling approach will provide a significant tool to manage the status of construction project, including progress and quality tracking. The project will enhance the related research field and also provide opportunities for further collaboration.

Researcher: Jian Chai

 

DIGITISING THE BUILT ENVIRONMENT

August 2018 - April 2019

Leverhulme Trust Int. Fellowship & TUM Vis. Professorship, £49,790

 

SeeBridge: AUTOMATED COMPILATION OF SEMANTICALLY RICH BIM MODELS OF BRIDGES

October 2015 - September 2017

ERA.NET Infravation, European Commission Grant, £414,119

SeeBridge (Semantic Enrichment Engine for Bridges) targets the development of a comprehensive solution for rapid and intelligent survey and assessment of bridges. Various advanced remote sensing technologies, including terrestrial laser scanner, high-resolution cameras mounted on mobile vehicle and drone, etc., will be used to rapidly and accurately capture the state of more than 5 bridges across the world and produce high density colored point cloud data.

A bridge object detection software will be developed to recognize distinct 3D solid geometry from the point cloud data. An expert system will be developed for classification of bridge components from the 3D solid model and for deduction of supplementary information concerning material types, internal bridge component, etc., using encoded bridge engineers’ knowledge. A damage measurement tool will also be devised to associate the identified defection to the model at the bridge component level. The output will be a Bridge Information Model that is semantically meaningful to sufficiently provide most of the information needed for decision-making concerning the repair, retrofit or rebuild of a bridge.

Researchers: Philipp Huethwohl and Ruodan Lu

 

DIGITALLY ENABLING THE DESIGN FOR MANUFACTURE, ASSEMBLY AND MAINTENANCE OF BRIDGES

April 2015 – March 2017

UK Technology Strategy Board Grant, PI/Grant Funding: Laing O’ Rourke, £1,199,983 

Bridges are largely designed as bespoke solutions with the majority of the work being carried out on site and in the case of improvements to and replacement of existing bridges involves disruption through lane closures and detours. The project's objectives are to develop an integrated digital delivery process for bridges and bridge parts. It addresses the whole lifecycle of bridges from identification and rationalisation of needs to manufacture, assembly, operation, maintenance and decommissioning.  The output is an interoperable set of digital tools, data schema and virtual prototyping processes that lead to the automated manufacture of a set of standardised, validated parts and sub-assemblies at a controlled price, configured virtually and in reality that are capable of meeting the requirements of the most common bridge types.

 
CIG: AUTOMATED AS-BUILT MODELLING OF THE BUILT INFRASTRUCTURE

September 2013 – August 2017

Marie Curie Actions, European Commission Grant, £78,914

Current automated methods and technologies for mapping and labeling existing infrastructure are not able to achieve a fully automated process that can generate as-built geometric infrastructure models. Although as-built modelling is significantly assisted by recent technological advancements, most of it heavily relies on manual inspection and analysis, which are error-prone and time-consuming. Further automation could be achieved with the help of object detection concepts. This research aims to automate the detection of the objects by creating new object detection methods from image and spatial data using computer vision and match the detected objects with standardized ones used in Building Information Modelling.

Researchers: Marianna Kopsida, Ioannis Anagnostopoulos.

 
URBAN SCALE BUILDING ENERGY NETWORK

October 2014 – September 2015

Engineering and Physical Sciences Council, PI / Grant Funding: James Keirstead, CE, Imperial, £28,868

The Climate Change Act 2008 requires a 34% cut in 1990 greenhouse gas emissions by 2020 and at least an 80% reduction in emissions by 2050. Residential and commercial buildings account for 25% and 18% of the UK's total CO2 missions respectively and therefore have a significant role to play in a national decarbonisation strategy. As the UK has some of the oldest and least efficient buildings in Europe, there is substantial scope for improving the efficiency of energy end-use within UK buildings. The overall aim of this project was to establish a network of academics and practitioners to discover the knowledge gaps and practical obstacles that inhibit the rapid improvement of the thermal performance of the UK building stock and to devise hypotheses of theoretically-feasible solutions that could be used to solve these problems.

 
COLLABORATION FOR RESEARCH & EDUCATION IN AUTO-GENERATION OF BUILDING INFORMATION MODELS

September 2012 – August 2015

Marie Curie Actions, European Commission Grant, £254,670

The purpose of this project was to facilitate international collaboration and transfer of knowledge between the AutoBIM consortium members (I.Brilakis, Cambridge; R.Sacks, Technion; S.Christodoulou, UofCyprus; M.Lourakis, FORTH; S.Savarese, UofM; J.Teizer, GATech) and to implement and test whether a novel framework can be successfully used to generate parametric building models of buildings, ranging from residential housing to industrial facilities, almost entirely automatically. The project’s most significant contributions was, not only the automation of several mundane and repetitive processes with the addition of visual and spatial pattern recognition concepts in the modelling workflow, but also the exchange of knowledge and the building of transatlantic research collaborations on cutting-edge and high-impact scientific projects through the exchange of interdisciplinary staff among partner institutions and joint training of research teams on thematic areas of common research interest.

Researcher: Bella Nguyen

RECIPROCAL RECONSTRUCTION AND RECOGNITION FOR MODELING OF CONSTRUCTED FACILITIES

September 2010 – August 2015

NSF Grant #1031329, £200,028

The research objective of this project was to evaluate whether a novel framework proposed by the PIs can progressively reconstruct a reinforced concrete frame structure into an object-oriented geometric model, for the purpose of automating the Building Information Model (BIM) making process of constructed facilities in a cost-effective manner. According to the proposed framework, the modeler videotapes the structure from all accessible angles to minimise occlusions. During this stage, the structural members (concrete columns and beams in this study) in the resulting stream of images are detected and their occupying region is marked in all images. These regions are used to establish correspondence at the object level across images, and solve the rough registration problem efficiently. Line-based structure from motion is then applied to the result to produce a rendered 3D view of the structure with the recognized regions marked. This loops back to the detection of structural members, which can now be also performed on the spatial data covered by the visually marked regions. The result is more robust element detection (by combining visual and spatial detection results), and consequently improved element matching and reconstruction. The resulting object-oriented model is an accurate 3D representation of the structure with the load bearing linear members detected. This model is provided to the modeler, who can then use it to complete the model making process.

Researchers: Abbas Rashidi and Guangcong Zhang

 
SBIR: A NOVEL VIDEO BASED SOFTWARE APPLICATION FOR AUTOMATIC ACCURATE ROOF SURVEYING

January 2013 – June 2013

NSF Grant #1248784, PI: Metalforming Inc., $150,000

This project investigated the technical and commercial feasibility of designing a video-based roof surveying software. Roof surveying is an essential activity in sheet metal roofing projects. Several technologies have been evolved over the years for this purpose; however, none of them are safe, inexpensive, automatic, and accurate enough at the same time. Tape measurement is therefore still the standard practice despite its apparent limitations. This project addressed this need by automatically generating an accurate 3Dwirediagram of a roof which includes necessary measurements. It is the world’s first video-based roof surveying software that fulfils all industry requirements (accuracy, simplicity, cost, safety, and efficiency). Compared to tape measuring, this project significantly reduces measuring costs and also eliminates the exposure of employees to fall hazards, thereby decreasing the very high number of occupational injuries and fall deaths which occur in the roofing industry (7% of private construction fatalities in 2009). The simplicity of the application removes the need for trained surveyors which are required for surveying with a total station.

Researcher: Habib Fathi

 

I-CORPS: VIDEOGRAMMETRIC ROOF SURVEYING SYSTEM FOR DIG. FAB. OF SHEET METAL ROOF PANELS

March 2012 – August 2013

NSF Grant #1217201, £32,679

A structured hypothesis/validation approach was being investigated in this project to develop a disposition plan for a videogrammetric surveying technology. The primary focus of this effort was on the possible use of this technology for as-built 3D documentation of construction sites; however, other potential markets such as on-site measurement of buildings, 3D visualization, augmented reality, etc. were also investigated.

Researcher: Habib Fathi.

 

OPTICAL SURVEYOR

January 2012 – December 2012

Georgia Research Alliance, £32,679

Detailed measurements are required at each step of the construction process to ensure the new installation, such as a roof or new countertop, fits. These are currently acquired either by hand measuring on the low end or using laser based devices on the high end. A machine vision-based technology is developed to facilitate this process and significantly reduce the cost of operation. The goal of this project was to commercialize this technology for creating 3D drawings of a structure or space.

Researcher: Habib Fathi.

 
VIDEOGRAMMETRIC BUILDING WIREFRAME CALCULATOR FOR SHEET METAL ROOFING

January 2011 – December 2012

Metalforming Inc., £80,931

Metalforming Inc. is the North American leader in metal building and architectural sheet metal technology. The company sells and services a CNC machine, called CINCO, which is able to automatically cut roll sheet metal into different pieces appropriate to cover a roof. The only input data to this machine is a 3D wirediagram which includes the perimeter of the roof in 3D space. Currently, this information is manually collected using a tape measure while total station surveying is also used in some projects. Our collaborative research aimed to completely automate this data collection process using a videogrammetric surveying technology. This way, the entire process of cutting roll metal sheets, from data collection to end product, was automated.

Researcher: Habib Fathi.
 

 

CAREER: VISUAL PATTERN RECOGNITION MODELS FOR REMOTE SENSING OF CIVIL INFRASTRUCTURE

August 2010 – August 2013

NSF Grant #0948415, £262,927

This was a project focused on fundamental research that enabled automated, model based recognition of construction objects. It entailed the creation of visual pattern recognition models for a variety of construction object types. The purpose was to assist as-built modelers of facilities by automatically recognizing the common and more frequent objects automatically, leaving only the specialty items to the hands of the modeler. The validation of this project was based on the software platform of Bentley Inc. , who was the industrial collaborator to this project. Results from this work can be found here.

Researcher: Stefania Radopoulou.

 

PROGRESSIVE SITE MODELING WITH VIDEOGRAMMETRY

August 2008 – July 2011

NSF Grant #0800170, £151,246

The objective of this research was to test the hypothesis that a mobile, calibrated set of high resolution video cameras can be used to acquire the spatial data of a construction site with the assistance of a novel videogrammetric method. Under this method, video streams are initially collected from the camera set that is progressively traversed around a construction site. The possible correspondences of each point in each video camera's view are computed (epipolar lines) and the corresponding points are matched using a novel window similarity matching method that compares the video frame along each epipolar line. Based on each match and the camera calibration, the depth value of each point is computed, and the depth map (point cloud) of the scene is generated. In each subsequent frame, all points with a previously identified correspondence in the other video camera's frame are tracked using established 2D point tracking techniques. The resulting point cloud at each frame is then converted to a 3D surface using intelligent proximity algorithms, and the visual data are overlaid to produce a photorealistic, rendered 3D surface.

Researcher: Habib Fathi.

GRS: PROGRESSIVE SITE MODELING WITH VIDEOGRAMMETRY

August 2009 – July 2011

NSF Grant #0943112, £24,169

This project was a supplement to the project above and aimed to engage an underrepresented graduate student, Ms. Stephanie German, in the core research of the parent grant. Her scope of work was including the activities needed to validate the point pair tracking and relative geo-referencing methods proposed originally. The supplement was requested due to an additional predecessor research activity (automate camera system calibration) that was discovered to be necessary after the first 6 months of tests, and was not taken into account in the original budget.

Researcher: Stephanie German.

Latest news

A fully funded PhD studentship is now available

30 April 2021

A fully funded 4-year EPSRC Industrial CASE PhD Studentship is available, commencing on 1 October 2021, to investigate methods for generating Digital...

A postdoc opportunity in Construction Information Technology sponsored by the EC H2020 OMICRON

13 April 2021

A 36-month position is being advertised for a Research Assistant/Associate in the Department of Engineering to work on the project titled "OMICRON...