Publications

Total BIM: Toward transforming construction

Year: 2024
Author(s): Oliver Disney
Publication Type: Licentiate thesis

Building Information Modeling (BIM) was expected to rapidly transform the construction industry, but its uptake has been unexpectedly slow. Furthermore, even state-of-the-art BIM projects have been challenged by hardware and software issues, limiting BIM’s implementation in the construction phase. This research explores an emerging approach to construction: Total BIM. Total BIM embraces BIM in its totality in that BIM is actively used in the construction phase by implementing model-based construction processes. It replaces 2D drawings as the legally binding source of information, enabling site workers to interact with modern cloud-based BIM software to create and extract necessary and relevant information.

Until recently, there has been a lack of real-world cases successfully implementing BIM as the single source of information for construction workers, which has hindered the possibility for researchers to explore the use of Total BIM in practice. However, this is beginning to change in Sweden and Norway, with the emergence of pioneering Total BIM projects. The purpose of this study has been to explore how Total BIM can be implemented as a single source of information across the design and construction phases of real-world projects. To achieve this, three in-depth case studies were conducted, collecting qualitative data from semi-structured interviews, observations, workshops, and more.

The findings, presented in four appended papers, demonstrate that implementing Total BIM in construction projects is possible and may even be preferred compared with traditional ways of working. Total BIM could serve as the missing link for advancing digitalization in the construction industry. Construction workers become an important part of structured data creation, through integrated processes such as requests for information (RFIs), controls, checklists and photos. This structured data enables new opportunities for informed, data-driven decision-making and site monitoring.

This research contributes rich empirical data from real-world case studies of Total BIM projects, illustrating how Total BIM overcomes limitations observed in previous state-of-the-art BIM projects. Additionally, it questions whether Total BIM could represent the digital disruption that the construction industry has been missing. For practitioners, this research provides real-world examples from Total BIM projects, demonstrating Total BIM implementation and highlighting key processes, while highlighting how Total BIM can create value.


A Review and Case Study of Neural Network Techniques for Automated Generation of High Level-of-Detail 3D City Models

Year: 2023
Author(s): Vasilis Naserentin, Georgios Spaias, Anestis Kaimakamidis, Nikos Pitsianis, Anders Logg
Publication Type: Paper in proceeding

The growing interest in creating digital twins of cities has sparked a surge in the development of detailed 3D models. In this paper we examine the current state-of-the-art in generating high-resolution 3D models of cities using neural network techniques. Additionally, we showcase the outcomes of two case studies that demonstrate the practical applications of these techniques in 3D city model generation. The first case study focuses on rooftop segmentation using publicly available Swedish cadastral data, while the second case study explores façade feature extraction using Google Street View data.


Towards a framework for railway network assets management based on BIM/GIS integration

Year: 2023
Author(s): Mattia Mangia, Carla Di Biccari, Mattias Roupé
Publication Type: Paper in proceeding

Complex infrastructures such as railway networks face increasing challenges related to resource allocation, external events, constraints, and demands. Therefore, it is crucial to optimize the Asset Management (AM) phase to ensure the value and functionality of the assets. The integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) can support this phase, but it can only yield benefits with a comprehensive approach that considers and addresses the specific needs and resources of the assets and their AM organization. The main benefits include improved data management, manipulation, information visualization and optimized resource allocation. This study describes an intermediate step towards developing a BIM/GIS integration framework for AM that can guide both researchers and practitioners. The framework aims to bridge theory and practice by incorporating insights from literature reviews and case studies. Its main objectives are to provide a comprehensive multi-stakeholder view and methods for effectively integrating BIM and GIS in this context. To develop the framework, the study employed focus groups, interviews, and practical BIM/GIS tests, which provided insights reported in this article. Furthermore, the study provides research directions for effective BIM/GIS integration in infrastructure AM.


A Grid-Based Methodology for the Assessment of Timedependent Building Damage at Large Scale

Year: 2023
Author(s): Pierre Wikby, Ezra Haaf, Ayman Abed, Lars Rosen, Jonas Sundell, Minna Karstunen
Publication Type: Preprint

Leakage into underground constructions can result in groundwater drawdown and time-dependent settlements in soft clay. In urban areas with spatial variability in geologic stratification, groundwater conditions and soil compressibility, differential settlements may occur, causing damage to buildings. Current methods for damage assessment tend to rely on 1D empirical formulations for settlement prediction, which are not representative for drawdown-induced settlements in heterogeneous environments. In this paper, 2D deformations were first computed using a coupled hydro-mechanical finite element model, simulating the time-dependent pore-pressure reduction in a confined aquifer. An advanced constitutive model was used to account for both creep and consolidation. The results were employed to train a metamodel, which considered spatial variability in stratification, resulting in time-dependent vertical settlements computed as a grid. Based on these results we propose an approach to integrate spatially distributed, grid-type settlement data into building damage assessments at city scale. The approach was applied to 215 buildings in central Gothenburg, Sweden by simulating scenarios of 10 kPa and 40 kPa pore pressure drawdown in the lower (confined) aquifer. The proposed methodology offers an effective way for damage assessments at a large scale, so that further investigations and mitigation measures can be targeted to the buildings at highest risk for damage.


The influence of parameter variability on subsidence

Year: 2023
Author(s): Pierre Wikby, Ayman Abed, Mats Karlsson, Jonas Sundell, Minna Karstunen
Publication Type: Paper in proceeding

Leakage into rock tunnels covered by thick soft clay deposits may cause a pore water pressure drop over large areas through underdrainage, resulting in settlement problems and potential damage to structures. In urban areas, heterogeneity in soil properties can be substantial. In this paper, a case study with a systematic sensitivity analysis combined with coupled hydro-mechanical finite element analyses was performed for three key parameters (overconsolidation ratio, vertical hydraulic conductivity and hydraulic anisotropy) considering one scenario of underdrainage. The results show that both the magnitude and uncertainty of settlements are strongly stratigraphy-dependent. The overconsolidation ratio contributed the most to the settlement uncertainty and the effect of vertical hydraulic conductivity was also found to be significant, while the changes in hydraulic anisotropy had negligible influence.


DTCC Builder: A mesh generator for automatic, efficient, and robust mesh generation for large-scale city modeling and simulation

Year: 2023
Author(s): Vasilis Naserentin, Anders Logg, Dag Wästberg
Publication Type: Journal article

Digital Twin Cities Centre (DTCC) Builder is a mesh generator for automatic, efficient, and robust mesh generation for large-scale city modeling and simulation. Using standard and widely available raw data sources in the form of point clouds and cadastral data, DTCC Builder generates high-quality 3D surface and volume meshes, suitable for both visualization and simulation. In particular, DTCC Builder is capable of generating large-scale, conforming tetrahedral volume meshes of cities suitable for finite element (FEM) simulation.


Evaluation of social facilities coverage: A case study of Sofia city

Year: 2022
Author(s): Stoyan Boyukliyski, Dessislava Petrova-Antonova, Sanjay Somanath
Publication Type: Paper in proceeding

In order to aid the decision making process related to the provision of public services as to maximize the benefits for society, it is crucial to evaluate the current social facilities demand in terms of spatial distribution and access. The paper aims to solve this problem by proposing a method for automated assessment of the coverage of public services within an urban region using a capacitated graph. The methodology abstracts residential buildings into demand nodes and public service buildings into supply nodes within a graph and then uses shortest distance calculations in order to balance the two, while prioritizing residential buildings based on distance. The paper is focused on creating a general pipeline that can be used on any type of public services, as long as a certain geospatial and demographic data are available. The method is described without referencing specific tools, but focusing on the general procedure. The procedure is then applied to the whole city of Sofa, focusing on assessing the coverage of kindergartens using the 15 minutes walking distance, followed by a brief discussion of results.


Roof Segmentation Towards Digital Twin Generation in LoD2+Using Deep Learning

Year: 2022
Author(s): N. Kolibarov, Dag Wästberg, Vasilis Naserentin, D. Petrova-Antonova, S. Ilieva, Anders Logg
Publication Type: Paper in proceeding

There is an increasing need for digital twins of cities and their base maps, 3D city models. Creating and updating these twins is not an easy task, so automating and streamlining the process is a field of active research. A significant part of the urban geometry is residential buildings and their roofs. Modeling of roofs for urban buildings can be divided into three main areas - building detection, roof recognition and building reconstruction. The building and roofs are segmented with the help of machine learning and image processing. Afterwards the extracted information is used to generate parametric models for the roofs using methods from computational geometry. The goal is to create correct virtual models of roofs belonging to many different types of buildings. In this study, a supervised deep learning approach is proposed for the segmentation of roof edges from a single orthophoto. The predicted features include the linear elements of roofs. The experiments show that, despite the small amount of training data, even in the presence of noise, the proposed method performs well on semantic segmentation of roofs with different shapes and complexities. The quality of the extracted roof elements for the test area is about 56% and 71% for mean intersection over union (IOU) and Dice metric scores, respectively. Copyright (C) 2022 The Authors.


AI-baserad segementering av fasader för att optimera renovering i en större skala

Year: 2021
Author(s): Sanjay Somanath, Yinan Yu, Nils Nordmark, Mola Ayenew, Liane Thuvander, Alexander Hollberg
Publication Type: Magazine article

Hur kan vi på ett automatiskt sätt skapa mer detaljerade 3D modeller av byggnader i digitala tvillingar och förbättra indata för att beräkna energibesparingspotentialer i befintliga byggnader? I en pilotstudie har vi undersökt hur maskininlärning kan användas för att extrahera information om fönstersättning och storlek i befintliga byggnader. Vi har utvecklat en modell som har “tränats” att känna igen och segmenterar fönster från bilder med byggnadsfasader och på så sätt skapa digitala och mer detaljerade data för befintliga byggnader. Vårt långsiktiga mål är att utveckla en helautomatisk metod för analyser av renoveringspotentialer för byggnader och fastighetsportföljer.


Data-Informed Urban Design: An Overview of the Use of Data and Digital Tools in Urban Planning and Design

Year: 2020
Author(s): Alexander Gösta, André Agi, Jacob Flårback, Jesper Karlsson, Ellen Simonsson
Publication Type: Journal article

This article aims to map how different digital tools can be useful for architects and how they might affect their work processes. Researchers and professionals were interviewed to investigate what they found valuable to measure, which methods they used within their analyses, as well
as the opportunities and risks they see for the future of the field with regards to digital tools. As part of the survey, a workshop was held with architects and project managers examining the possibilities of connecting existing methods and tools to the sustainability certification system,
City Lab Action Guide, and through that, to achieve a more ambitious set of sustainability goals for the projects. Findings from the study indicate that there are risks associated with giving data an increasingly important role in the design work. A working model never provides the full truth
but is inherently limited by its constraints. It is important to acknowledge that all angles and aspects of a problem can never be represented in a model. Another possible risk identified lies in the quality of, and access to, data. In a scenario where data plays an increasingly important
role, it is not only the quality of the datasets that is of utmost importance, but it is equally important that the urban planners who request the analyses ask the questions first, and then collect the necessary data, instead of vice versa.


Seminar Series

Videos