Developing a BIM-based circularity assessment tool

▪ The construction industry is responsible for large amounts of resource consumption and waste production. The “Circular Economy” concept aims to decouple economic growth from materials extraction. How to quantify circularity performance – in a Building Information Modelling (BIM)-based environment – is growing in interest and methodological debate.

▪ Research goal: “Provide project stakeholders actual insights into circularity performance of their construction projects from early design phases to construction phases, by developing a BIM-based circularity assessment tool”.

Methods

▪ Design Science Methodology with three iterative design cycles (see picture below).

▪ Two cases (a renovation and new-built project) located at the University of Twente.

▪ Literature and document review; Interviews with project stakeholders.

Results

A BIM-based circularity assessment tool is developed according to the Input-Processing-Output model (see picture on the right):

▪ Input: A combination of input sources from BIM models and an external circularity database; Open standards (NL-SfB, NAA.KT and ETIM) are used to structure and manage circularity-related information.

▪ Processing: An assessment module in which three different calculation models for different levels of detail (LODs) are available

▪ Output: Circularity score supported with 3D colour coding and 2D analysis charts, presented via a Graphic User Interface.

Discussion

▪ A circularity assessment method should consider the level of information availability in different project phases.

▪ BIM is a useful in supporting information collection and management, to smoothen the process of circularity assessment

▪ More insight is still needed into how the circularity-related information for making circularity feasible and transparent can be recorded in public standards.

*Contact: l.jiang@utwente.nl

Data-Driven Prediction and Reduction of Excavation Damages

Excavation Damage, Machine Learning

Jiarong Li (j.li-5@utwente.nl), Prof. dr. ir. A.G. (André) Dorée, Dr. ir. L.L. (Léon) olde Scholtenhuis, CME | CEM | ET | University of Twente, Netherlands

Introduction

▪ More than 40000 excavation damages to underground cables and pipelines per year. Huge direct costs as well as serious economic and societal consequences

▪ Lack of data-driven projects. Existing data-driven projects using limited dataset.

▪ Project Objectives:

▪ To explore causal factors of excavation damages.

▪ To explore existing datasets useful for the prediction.

▪ To build a machine learning model to predict the probability of excavation damage occurrence.

▪ To design a component in Kadaster Kabels en Leidingen Informatie Centrum (KLIC) system to apply the prediction model.

▪ To summarize the significant factors leading to damages and provide damage prevention strategies.

Methods

▪ Design methodologies: Design cycle, Crisp DM cycle

▪ Modelling: Logistic regression, XGBoost, ANN…

Preliminary Outcomes of Factor Exploration and Data Collection

▪ Factors: utility assets density of each type, project type, project size, project duration, polygon complexity of the digging area, weather, founding date of the excavation company, size of the excavation company, soil type, landuse type, tree density, average building ages.

▪ Datasets: schademelding, graagmelding, IMKL, KNMI weather data, KVK company data, BRO, BAG, bestandbodemgebruik, Bomen

System Functions