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1. Invariant Signatures of Architecture, Engineering, and Construction Objects to Support BIM Interoperability between Architectural Design and Structural Analysis

   https://doi.org/10.1061/(ASCE)CO.1943-7862.0001943  

   Wu, J. ( January 2021 , Journal of construction engineering and management)

   de la Garza, J.M. (Ed.)

   There has been an increasing demand in building information modeling (BIM) for structural analysis. However, model exchange between architectural software and structural analysis software, which is an essential task in a construction project, is not fully interoperable yet. Various studies showed missing information and information inconsistency problems during conversion of models between different software; the lack of foundational methods enabling a seamless BIM interoperability between architectural design and structural analysis is evident. To address this gap and facilitate more use of BIM for structural analysis, the authors develop invariant signatures for architecture, engineering, and construction (AEC) objects and propose a new data-driven method to use invariant signatures for solving practical problems in BIM applications. The invariant signatures and the data-driven method were tested in developing the interoperable BIM support tool for structural analysis through an experiment. Ten models were created/adopted and used in this experiment, including five models for training and five models for testing. An information validation and mapping algorithm was developed based on invariant signatures and training models, which was then evaluated in the testing models. Compared with a manually created gold standard, results showed that the desired structural analysis software inputs were successfully generated using the algorithm with high accuracy. The invariant signatures of AEC objects can therefore be expected to serve as the foundation of seamless BIM interoperability. 

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2. A New Framework to Address BIM Interoperability in the AEC Domain from Technical and Process Dimensions

   Ren, Ran ; Zhang, Jiansong ( January 2021 , Advances in Civil Engineering Online)

   Building Information Modelling (BIM) is an integrated informational process and plays a key role in enabling efficient planning and control of a project in the Architecture, Engineering, and Construction (AEC) domain. Industry Foundation Classes (IFC)-based BIM allows building information to be interoperable among different BIM applications. Different stakeholders take different responsibilities in a project and therefore keep different types of information to meet project requirements. In this paper, the authors proposed and adopted a six-step methodology to support BIM interoperability between architectural design and structural analysis at both AEC project level and information level, in which: (1) the intrinsic and extrinsic information transferred between architectural models and structural models were analyzed and demonstrated by a Business Process Model and Notation (BPMN) model that the authors developed; (2) the proposed technical routes with different combinations, and their applications to different project delivery methods provided new instruments to stakeholders in industry for efficient and accurate decision-making; (3) the material centered invariant signature with portability can improve information exchange between different data formats and models to support interoperable BIM applications; and (4) a developed formal material information representation and checking method was tested on a case study where its efficiency was demonstrated to outperform: (1) proprietary representations and information checking method based on a manual operation, and (2) MVD-based information checking method. The proposed invariant signatures-based material information representation and checking method brings a better efficiency for information transfer between architectural design and structural analysis, which can have significant positive effect on a project delivery, due to the frequent and iterative update of a project design. This improves the information transfer and coordination between architects and structural engineers and therefore the efficiency of the whole project. The proposed method can be extended and applied to other application phases and functions such as cost estimation, scheduling, and energy analysis. 

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3. A machine learning-based approach for building code requirement hierarchy extraction

   Zhang, Ruichuan ; El-Gohary, Nora ( January 2019 , 2019 CSCE Annual Conference)

   Most of the existing automated code compliance checking (ACC) methods are unable to fully automatically convert complex building-code requirements into computer-processable forms. Such complex requirements usually have hierarchically complex clause and sentence structures. There is, thus, a need to decompose such complex requirements into hierarchies of much smaller, manageable requirement units that would be processable using most of the existing ACC methods. Rule-based methods have been used to deal with such complex requirements and have achieved high performance. However, they lack scalability, because the rules are developed manually and need to be updated and/or adapted when applied to a different type of building code. More research is, thus, needed to develop a scalable method to automatically convert the complex requirements into hierarchies of requirement units to facilitate the succeeding steps of ACC such as information extraction and compliance reasoning. To address this need, this paper proposes a new, machine learning-based method to automatically extract requirement hierarchies from building codes. The proposed method consists of five main steps: (1) data preparation and preprocessing; (2) data adaptation; (3) deep neural network model training for dependency parsing; (4) automated requirement segmentation and restriction interpretation based on the extracted dependencies; and (5) evaluation. The proposed method was trained using the English Treebank data; and was tested on sentences from the 2009 International Building Code (IBC) and the Champaign 2015 IBC Amendments. The preliminary results show that the proposed method achieved an average normalized edit distance of 0.32, a precision of 89%, a recall of 76%, and an F1-measure of 82%, which indicates good requirement hierarchy extraction performance. 

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4. A new schema of logic representation and reasoning for automated building code compliance checking.

   Yang, F. ; Zhang, J. ; Chen, Y. ; and Debs, L. ( January 2022 , Proceedings of the GPEA Polytechnic Summit 2022: Session Papers)

   Steinmetz, A. (Ed.)

   Manual building code compliance checking is a time-consuming, labor-intensive and error-prone process. Automated logic-based reasoning is an essential step in the automation of this process. There have been previous studies using logic programming languages for automated logic-based reasoning to support automated compliance checking (ACC) of building designs with building codes. As a high-performance implementation of the standard logic programming language, B-Prolog was widely used in these studies. However, due to the support of dynamic predicates and user-defined operators, the predicates’ functions vary according to different user definitions; therefore, B-Prolog is sometimes not reliable for building code reasoning. As a more expressive, scalable, and reliable alterative to B-Prolog, Picat, a logic-based multi-paradigm programming language, provides a new and potentially more powerful platform for automated logic-based reasoning in ACC. To explore the potential value of Picat in ACC, in this study, the authors compared Picat and B-Prolog performance in automatically checking 20 requirement rules in the 2015 International Building Code. The experimental results showed that the automated checking for building codes in the B-Prolog version was faster than that in the Picat version, whereas the Picat version was more reliable than the B-Prolog version. This could be the result of B-Prolog using unifica-tion and Picat using pattern matching for indexing rules. More potential applications of Picat in ACC domain need further research. Furthermore, this schema could be used in the teaching of ACC to graduate construction students, illustrating the need to focus on the reliability, predictability and scalability of the process, in order to provide a practical solution to improving code compliance checking processes. 

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5. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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