17 results found
Soman RK, Molina-Solana M, 2022, Automating look-ahead schedule generation for construction using linked-data based constraint checking and reinforcement learning, Automation in Construction, Vol: 134, Pages: 1-16, ISSN: 0926-5805
Look-ahead planning is the stage in construction planning where information from diverse sources is integrated and plans developed for the next six/eight weeks. Poor planning of construction site activities at this stage often results in cost overruns and schedule delays. This work presents a novel Look-Ahead Schedule (LAS) generation method, which uses reinforcement learning and linked-data based constraint checking within the reward, to address the issues associated with manual look-ahead planning and help construction professionals efficiently plan construction activities at this stage. Our proposal can generate conflict-free LAS significantly faster than conventional methods, demonstrating its capability as a decision support tool during look-ahead planning meetings. Therefore, this paper extends existing knowledge in the construction informatics domain by demonstrating the application of reinforcement learning to aid data-driven look-ahead planning.
Kuttantharappel Soman R, 2021, Bridging the gap between information management and advanced work packaging: AWP ontology, 38th International Conference of CIB W78, Publisher: ITC Digital library, Pages: 1-8, ISSN: 2706-6568
Wallbaum M, Soman RK, 2021, Towards real-time Scan-versus-BIM :methods applications and challenges, 2021 European Conference on Computing in Construction, Publisher: University College Dublin, ISSN: 2684-1150
Farghaly K, Soman RK, Whyte J, 2021, Visualizing real-time information through a construction production control room, 2021 European Conference on Computing in Construction, Publisher: University College Dublin, Pages: 1-8
Extending current work on visualisation in the Architecture, Engineering and Construction (AEC) sector, this paper describes an industry-led collaborative research and innovation project to develop and use a control room on the construction site. The work is inspired by NASA mission operations, with its large-scale visual display. It addresses the challenges of visualizing real-time construction data. Working with a main contractor, technology companies and other researchers, we first give an overview of the progress of the overall project to date and discuss our contributions on requirements, real-time analytics and visualization. We conclude by discussing the contribution to work on visualizing construction.
Mijic A, Whyte J, Fisk D, et al., 2021, The Centre for Systems Engineering and Innovation – 2030 vision and 10-year celebration
The 2030 vision of the Centre is to bring Systems Engineering and Innovation to Civil Infrastructure by changing how cross-sector infrastructure challenges are addressedin an integrated way using principles of systems engineering to maximise resilience, safety and sustainability in an increasingly complex world.We want to better understand the environmental and societal impacts of infrastructure interventions under uncertainty. This requires a change in current approaches to infrastructure systems engineering: starting from the natural environmentand its resources, encompassing societaluse of infrastructure and the supporting infrastructure assets and services.We argue for modelling that brings natural as well as built environments within the system boundaries to better understand infrastructure and to better assess sustainability. We seethe work as relevant to both the academic community and to a wide range of industry and policy applications that are working on infrastructure transition pathways towards fair, safe and sustainable society.This vision was developed through discussions between academics in preparation for the Centre for Systems Engineering and Innovation (CSEI) 10 years celebration. These rich discussions about the future of the Centre were inspired by developing themes for a celebration event, through which we have summarised the first 10 years of the Centre’s work and our vision for the future and identified six emerging research areas.
Soman R, Molina Solana M, Whyte J, 2020, Linked-Data based Constraint-Checking (LDCC) to support look-ahead planning in construction, Automation in Construction, Vol: 120, ISSN: 0926-5805
In the construction sector, complex constraints are not usually modeled in conventional scheduling and 4D building information modeling software, as they are highly dynamic and span multiple domains. The lack of embedded constraint relationships in such software means that, as Automated Data Collection (ADC) technologies become used, it cannot automatically deduce the effect of deviations to schedule. This paper presents a novel method, using semantic web technologies, to model and validate complex scheduling constraints. It presents a Linked-Data based Constraint-Checking (LDCC) approach, using the Shapes Constraint Language (SHACL). A prototype web application is developed using this approach and evaluated using an OpenBIM dataset. Results demonstrate the potential of LDCC to check for constraint violation in distributed construction data. This novel method (LDCC) and its first prototype is a contribution that can be extended in future research in linked-data, BIM based rule-checking, lean construction and ADC.
Shi F, K Soman R, Han J, et al., 2020, Addressing adjacency constraints in rectangular floor plans using Monte-Carlo Tree Search, Automation in Construction, Vol: 115, ISSN: 0926-5805
Manually laying out the floor plan for buildings with highly-dense adjacency constraints at the early design stage is a labour-intensive problem. In recent decades, computer-based conventional search algorithms and evolutionary methods have been successfully developed to automatically generate various types of floor plans. However, there is relatively limited work focusing on problems with highly-dense adjacency constraints common in large scale floor plans such as hospitals and schools. This paper proposes an algorithm to generate the early-stage design of floor plans with highly-dense adjacency and non-adjacency constraints using reinforcement learning based on off-policy Monte-Carlo Tree Search. The results show the advantages of the proposed algorithm for the targeted problem of highly-dense adjacency constrained floor plan generation, which is more time-efficient, more lightweight to implement, and having a larger capacity than other approaches such as Evolution strategy and traditional on-policy search.
K Soman R, Whyte J, 2020, Codification challenges for data science in construction, Journal of Construction Engineering and Management, Vol: 146, Pages: 04020072-1-04020072-18, ISSN: 0733-9364
New forms of data science, including machine learning and data analytics, are enabled by machine-readable informationbut are not widely deployed in construction. Aqualitative study of information flow in three projects usingBuilding Information Modelling (BIM) in the late designand construction phaseis used to identify the challenges of codification whichlimit the application of data science.Despite substantial efforts to codify information with ‘Common Data Environment(CDE)’ platforms to structure and transfer digital information within and between teams, participants work across multiple media in both structured and unstructured ways. Challenges of codification identified in this paper relate to software usage (interoperability, translation, modelling, and file-based sharing), information sharing (unstructured information, document control, workarounds, process change,and multiple CDEs), and construction process information(loss of constraints and low level of detail). This paper contributes to the current understanding of data science in construction by articulating the codification challenges and their implications for data quality dimensions,such as accuracy, completeness, accessibility, consistency, timeliness, and provenance.It concludes with practical implications for developingand using machine-readable information and directions for research to extract insight from data and support future automation.
K Soman R, 2019, Modelling construction scheduling constraints using shapes constraint language (SHACL), 2019 European Conference on Computing in Construction, Publisher: University College Dublin
This paper presents a new approach for modellingconstruction scheduling constraints using ShapesConstraint Language. Current modelling approachesfocuses on modelling precedence and discrete constraintsat master planning or phase planning level and lacks theability to model complex constraints at look aheadplanning level. Proposed modelling approach addressesthis limitation. Precedence constraints, discrete resourcecapacity constraints, disjunctive constraints and logicalconstraints are modelled using shapes constraint languagefor a simple lifting problem in this paper. The modelledconstraints were tested, and the constraints model wasable to identify the violations effectively and produce avalidation report.
K Soman R, 2019, Linked-data based dynamic constraint solving framework to support look-ahead-planning in construction, 36th CIB W78 2019 Conference
Balasubrahmaniam M, Kuttantharappel Soman R, Whyte J, et al., 2019, Temporality, innovation and megaproject-to-megaproject learning across continents in the case of Crossrail and Nagpur Metro, 35th European Group for Organizational Studies Colloqium
Senthilvel M, K Soman R, Mahalingam A, et al., 2018, Towards Digital Delivery of Metro-rail Projects in India, The 7th World Construction Symposium
K Soman R, Birch D, Whyte J, 2017, Framework for shared visualization and real-time information flow to the construction site, 24th International Workshop on Intelligent Computing in Engineering
Senthilvel M, K Soman R, Varghese K, 2017, Comparison of Handheld Devices for 3D Reconstruction in Construction, 34th international symposium on automation and robotics in construction
K Soman R, Whyte J, 2017, A Framework for Cloud-Based Virtual and Augmented Reality using Real-time Information for Construction Progress Monitoring, Lean and Computing in Construction Congress (LC3).
K Soman R, Raphael B, Varghese K, 2017, A System Identification Methodology to monitor construction activities using structural responses, Automation in Construction, Vol: 75, Pages: 79-90, ISSN: 0926-5805
K Soman R, Raphael B, Varghese K, 2015, Sensor Placement to Monitor Launching Girder Operations in Segmental Construction, 32nd International Symposium on Automation and Robotics in Construction and Mining
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