Linked-Data based Constraint-Checking (LDCC) to support look-ahead planning in construction

This is a substantially extended and enhanced version of the paper presented at the CIB W78 Annual Conference held at Northumbria University in Newcastle UK in September 2019. We would like to acknowledge the editorial contributions of Professor Bimal Kumar of Northumbria University and Dr. Farzad Rahimian of Teesside University in the publication of this paper. The first author's PhD research is co-funded by Bentley systems the UK and through a Skempton Scholarship, the Department of Civil and Environmental Engineering, Imperial College London. During this work, the first author was also supported by the PhD enrichment scholarship from The Alan Turing Institute. The authors acknowledge the supervisory inputs offered by Dr. David Birch and Dean Bowman and also acknowledges the guidance offered by Prof. Jakob Beetz. The second author is supported by the European Commission (H2020 MSCA-IF ga. No. 754446). The third author acknowledges the support of Laing O'Rourke and the Royal Academy of Engineering for cosponsoring her Professorship.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.Bentley systems the UKSkempton Scholarship, the Department of Civil and Environmental Engineering, Imperial College LondonAlan Turing InstituteEuropean Commission 754446Laing O'RourkeRoyal Academy of Engineering - U

A smart sewer asset information model to enable an ‘Internet of Things’ for operational wastewater management

Real-time prediction of flooding is vital for the successful future operational management of the UK sewerage network. Recent advances in smart infrastructure and the emergence of the Internet of Things (IoT), presents an opportunity within the wastewater sector to harness and report in real-time sewer condition data for operation management. This study presents the design and development of a prototype Smart Sewer Asset Information Model (SSAIM) for an existing sewerage network. The SSAIM, developed using Industry Foundation Class version 4 (IFC4) an open neutral data format for BIM, incorporates distributed smart sensors to enable real-time monitoring and reporting of sewer asset performance. Results describe an approach for sensor data analysis to facilitate the real-time prediction of flooding