Building Information Modeling (BIM) produces three-dimensional object-oriented models of buildings combining the geometrical information with a wide range of properties about materials, products, safety and so on. BIM is slowly but inevitably revolutionizing the architecture, engineering, and construction (AEC) industry. Buildings need to be compliant with regulations about stability, safety, and environmental impact. Manual compliance checking is tedious and error-prone, and amending flaws discovered only at construction time causes huge additional costs and delays. Several tools can check BIM models for conformance with rules/guidelines. For example, Singapore’s CORENET e-Submission System checks fire safety. But since the current BIM exchange format only contains basic information of building objects, a separate, ad-hoc model pre-processing is required to determine, e.g., evacuation routes. Moreover, they face difficulties in adapting existing built-in rules and/or adding new ones (to cater for building regulations, that can vary not only among countries but also among parts of the same city), if at all possible.<br>We propose the use of logic-based executable formalisms (CLP and Constraint ASP) to couple BIM models with advanced knowledge representation and reasoning capabilities. Previous experience shows that such formalisms can be used to uniformly capture and reason with knowledge (including ambiguity) in a large variety of domains. Additionally, incorporating checking within design tools makes it possible to ensure that models are rule-compliant at every step. This also prevents erroneous designs from having to be (partially) redone, which is also costly and burdensome. To validate our proposal, we implemented a preliminary reasoner under CLP(Q/R) and ASP with constraints and evaluated it with several BIM models.
This webinar is going to cover what is a digital twin and how all stakeholders can benefit from their functionality. You will learn how model-based systems engineering enables digital engineering. Your host will discuss use cases, a realistic look at digital engineering and digital twins, and how you can use Innoslate to get started.
The Agenda
Here's what we're covering.
What is a Digital Twin
Benefits of Digital Twin
The Digital Engineering Path Enabled by MBSE
AR + MBSE Software
A More Realistic Digital Twin
Getting You Started with Digital Twins
Question Answer Session
Software_effort_estimation for Software engineering.pdfsnehan789
calculating software effort estimation for subjects like software engineering and software project management all according to your college preference on the subject
Jacobs has used Endeavour (AVEVA NET) for more than 12 years for delivery of project data. The use has been primarily driven by customer or contract requirements for data handover, but over time both Jacobs’ project teams and customers have recognized the value of having trustworthy and complete data at the completion of a project, and is giving a focused effort to execute data-centric projects moving forward. To support this, Jacobs is implementing AVEVA Engineering to drive a data-centric collaboration between disciplines to enable greater work efficiencies. This game-changing approach using Endeavour and AVEVA Engineering will provide data alignment across the full project spectrum of EPC delivery.
Presented by: Marc-Henri Cerar—Jacobs
Discover how AVEVA can transform your business today
www.aveva.com
Delivering Asset Management for Infrastructure Projects by Liam Gallagher, Ja...AVEVA Group plc
This document summarizes how Jacobs uses integrated AVEVA Technologies to manage engineering data and project information for infrastructure projects. It discusses the challenges of meeting client requirements, regulations, and managing large amounts of data from multiple sources and disciplines. The solution involves using AVEVA's ISM, Engineering, and NET platforms to import data from various sources, supplement it in a controlled environment, and automatically generate schedules, handover documents, and asset management information for clients. The summary provides an overview of Jacobs' proof of concept and pilot project using these tools to manage data throughout the project lifecycle.
The document discusses the PipeFab plugin for Revit, which automates pipe fabrication and drafting. It reduces production of installation drawings by 60%. The plugin lists pipes, exports data to Excel, and generates CNC files. It provides direction labels and exports cutting sheets to Excel for easier review. The document also discusses several other BIM projects for facilities in Saudi Arabia.
Vimala Gadegi is a Project Leader at Cyient with over 9 years of experience in information technology. She has extensive experience developing .NET applications using technologies like C#, ASP.NET, SQL Server, and JavaScript. Some of her responsibilities include project estimation, analysis, design, coding, testing, and managing project teams. She has led projects for clients in various domains including transportation, rail signaling, and engineering design automation.
This webinar is going to cover what is a digital twin and how all stakeholders can benefit from their functionality. You will learn how model-based systems engineering enables digital engineering. Your host will discuss use cases, a realistic look at digital engineering and digital twins, and how you can use Innoslate to get started.
The Agenda
Here's what we're covering.
What is a Digital Twin
Benefits of Digital Twin
The Digital Engineering Path Enabled by MBSE
AR + MBSE Software
A More Realistic Digital Twin
Getting You Started with Digital Twins
Question Answer Session
Software_effort_estimation for Software engineering.pdfsnehan789
calculating software effort estimation for subjects like software engineering and software project management all according to your college preference on the subject
Jacobs has used Endeavour (AVEVA NET) for more than 12 years for delivery of project data. The use has been primarily driven by customer or contract requirements for data handover, but over time both Jacobs’ project teams and customers have recognized the value of having trustworthy and complete data at the completion of a project, and is giving a focused effort to execute data-centric projects moving forward. To support this, Jacobs is implementing AVEVA Engineering to drive a data-centric collaboration between disciplines to enable greater work efficiencies. This game-changing approach using Endeavour and AVEVA Engineering will provide data alignment across the full project spectrum of EPC delivery.
Presented by: Marc-Henri Cerar—Jacobs
Discover how AVEVA can transform your business today
www.aveva.com
Delivering Asset Management for Infrastructure Projects by Liam Gallagher, Ja...AVEVA Group plc
This document summarizes how Jacobs uses integrated AVEVA Technologies to manage engineering data and project information for infrastructure projects. It discusses the challenges of meeting client requirements, regulations, and managing large amounts of data from multiple sources and disciplines. The solution involves using AVEVA's ISM, Engineering, and NET platforms to import data from various sources, supplement it in a controlled environment, and automatically generate schedules, handover documents, and asset management information for clients. The summary provides an overview of Jacobs' proof of concept and pilot project using these tools to manage data throughout the project lifecycle.
The document discusses the PipeFab plugin for Revit, which automates pipe fabrication and drafting. It reduces production of installation drawings by 60%. The plugin lists pipes, exports data to Excel, and generates CNC files. It provides direction labels and exports cutting sheets to Excel for easier review. The document also discusses several other BIM projects for facilities in Saudi Arabia.
Vimala Gadegi is a Project Leader at Cyient with over 9 years of experience in information technology. She has extensive experience developing .NET applications using technologies like C#, ASP.NET, SQL Server, and JavaScript. Some of her responsibilities include project estimation, analysis, design, coding, testing, and managing project teams. She has led projects for clients in various domains including transportation, rail signaling, and engineering design automation.
Supporting Architectural Variabiality in Software Product LinesJaime Chavarriaga
This document discusses supporting architectural variability in software product lines. It begins with an overview of software product lines and product line architectures. It then discusses implementing variability at the architectural level using tactics and design patterns. Tactics aim to achieve certain quality attributes, and there are theories linking concerns to tactics and designs. Feature models can be used to represent relationships between tactics and design alternatives. Tactics can also be implemented using configuration options like in cloud computing platforms. Relating tactics to configuration options allows automatically deriving architectural designs based on quality attribute requirements.
Building product suggestions for a BIM model based on rule sets and a semant...Gonçal Costa Jutglar
The architecture, engineering and construction (AEC) industry today relies on different information systems and computational tools built to support and assist in the building design and construction. However, these systems and tools typically provide this support in isolation from each other. A good combination of these systems and tools is beneficial for a better coordination and information management. Semantic web technologies and a Linked Data approach can be used to fulfil this aim. In this paper, we indicate how these technologies can be applied for one particular objective, namely to check a building information model (BIM) and make suggestions for that model regarding the building elements. These suggestions are based on information obtained from different data sources, including a BIM model, regulations and catalogues of locally available building components.
Meetup 21/9/2017 - Image Recogonition: onmisbaar voor een slimme stad?Digipolis Antwerpen
1) Image recognition and computer vision technologies can enable various smart city applications like crowd behavior analysis, traffic analysis, and thermal signature tracking.
2) Autonomous systems that use computer vision and machine learning can perceive their environment and act independently to help during disasters by providing survivors and emergency personnel with locating information.
3) MATLAB provides tools for computer vision, machine learning, and deep learning that can help develop prototypes and applications for smart cities from idea to product.
Faisal Suleman is seeking a position that allows him to utilize his experience and skills to contribute to a company's growth. He has over 15 years of experience in business intelligence tools such as Siebel Analytics, Oracle OBIEE, and QlikView. He has led several data warehouse projects for companies in industries such as automotive and auction. Faisal has a bachelor's degree in computer science and commerce, and is Oracle Business Intelligence Applications 7 for ERP certified.
This presentation shares his insights on design simulation and the new role of the simulator in Simulator Assisted Engineering. For more information, go to GSES.com, email info@gses.com, or call 800-638-7912. You can also follow GSE at @GSESystems and Facebook.com/GSESystems.
Software Application Presentation SlideLee Pei Gie
The document provides information on various BIM QS computer software including Glodon, CostX, Vico Office, and Nomitech Costos. It discusses the features, functions, and limitations of each software. It also compares BIM, manual measurement, and CAD measurement in terms of benefits and constraints during various project stages from briefing to post-construction. Overall, the document provides a comprehensive overview and comparison of BIM QS software and measurement methods.
1) BIM software provides benefits throughout the project lifecycle from planning to construction and facility management. It allows for improved visualization, coordination, estimation and resource efficiency.
2) However, BIM also faces limitations such as the need for experienced users, high costs, disruption to traditional processes, and challenges with data sharing between stakeholders.
3) While BIM streamlines tasks like quantity take-off, it cannot account for all construction cost variables and may be time consuming for scheduling. Experienced teams are required to leverage its full capabilities.
The document summarizes a presentation given by Carl Collins on March 22nd 2018 about digital engineering and building information modeling (BIM). The presentation covered an introduction to UK BIM standards and requirements, how simulation fits into the BIM process, and key challenges. It discussed how BIM creates a virtual 3D model that is rich in data to inform decisions throughout the project lifecycle. The role of data in the design process was also examined from strategic definition through to building operation.
Jason Chen has over 19 years of experience in analog, digital and mixed-signal CAD development as well as management experience. He has led teams of up to 6 developers in successfully delivering layout verification runs and parameterized cells for over 10 process technologies on a 3-week cycle. As a CAD Engineering Manager, he managed teams that developed physical verification runsets and parasitic extraction runsets to support over 20 process technologies. He has extensive experience in Cadence and Synopsys tools as well as process design kit development and management.
Fiatech 2014 - Big BIM Implementation, Zuhair HaddadCCT International
This document provides details on a big BIM implementation for the Abu Dhabi International Airport Midfield Terminal Building project. It outlines CCC's BIM strategy, which included employing additional BIM engineers well before the project award to allow proper training. CCC utilized established BIM tools and workflows to facilitate integration with project controls. Notable benefits realized through BIM implementation included significant cost and time savings through clash detection and material take-offs, as well as improved construction planning and coordination through use of 4D simulations and a progressive interface model.
The document outlines an ASIC design flow using Mentor Graphics tools and describes 10 experiments for digital and analog circuit design. The design flow involves using Pyxis Schematic for circuit design, Pyxis Layout for layout, and Calibre for verification. The 10 experiments include designing basic gates, adders, latches, counters, static RAM cells, and differential amplifiers. The goal is to take students through the full ASIC design flow from circuit design to tapeout.
The document discusses the development of a computer aided critical lift planning system using parametric modeling software Autodesk Inventor. Existing critical lift planning tools have limitations and do not fully automate the planning process. The system developed in Autodesk Inventor aims to improve on previous tools by incorporating additional features like clearance checking, bearing pressure calculations, and 3D simulation of single and multiple crane lifts. The architecture of the new system is presented, which uses Autodesk Inventor for 3D modeling, Visual Basic for the interface and programming, and Microsoft Access for storing load chart data. The system is meant to make the critical lift planning process more efficient and reliable.
Nesma autumn conference 2015 - Is FPA a valuable addition to predictable agil...Nesma
This document discusses using Function Point Analysis (FPA) as a metric for Agile software projects. It provides context on replacing an existing trading system and outlines an architecture and development approach using Agile/Scrum. Metrics are proposed for use at the sprint level and cumulatively, including function points, story points, lines of code, and productivity rates. FPA is argued to provide benefits for scope management, benchmarking, and proving productivity and quality for Agile projects. Contracting based on function points is also discussed.
Architecture vs. Design in Agile: What’s the Right Answer?TechWell
Is architecture the same as preliminary design in agile? It shouldn't be. Do we create architecture up front, then do iterative development after the architecture is done? That is edging back toward waterfall. Can you explain the purpose of the architecture in just two or three statements? Anthony Crain says that when he asks that question, he gets either verbose answers or blank stares. So Anthony shares an elegantly simple two bullet explanation of what an architecture does. Explore the models architects and designers should produce and learn why these models are so important to keep separate. Understand why it is vital to separate functional from nonfunctional requirements and how this affects architecture, design, and even code and test. Explore what a conceptual architectural model should look like vs. a physical one, and for the conceptual design model vs. a physical one—and the timing of all four models. Finally, examine the impact of iterative development on architecture.
The document provides a resume for S. kemy, who is applying for Chief Technical Architect and related roles. It summarizes their 18.5 years of experience in designing and developing applications using technologies like .NET, SQL Server, Azure, and open source technologies. It details their experience as a .NET Architect, Mobile Architect, and MEAN.JS Architect. The resume lists their technical skills and strengths, including architectural design, frameworks, performance optimization, and involvement across the SDLC. It also provides details on 4 past projects with responsibilities and technologies used.
Qualcomm Webinar: Solving Unsolvable Combinatorial Problems with AIQualcomm Research
How do you find the best solution when faced with many choices? Combinatorial optimization is a field of mathematics that seeks to find the most optimal solutions for complex problems involving multiple variables. There are numerous business verticals that can benefit from combinatorial optimization, whether transport, supply chain, or the mobile industry.
More recently, we’ve seen gains from AI for combinatorial optimization, leading to scalability of the method, as well as significant reductions in cost. This method replaces the manual tuning of traditional heuristic approaches with an AI agent that provides a fast metric estimation.
In this presentation you will find out:
Why AI is crucial in combinatorial optimization
How it can be applied to two use cases: improving chip design and hardware-specific compilers
The state-of-the-art results achieved by Qualcomm AI Research
Niti Srivastava is seeking opportunities as an Application Developer with 2.7 years of experience working with IBM India Pvt Ltd. She has experience in COBOL, JCL, VSAM, DB2, and other mainframe technologies. She has worked on projects for clients like National Grid involving gas billing migration, data extraction, and system conversions. Niti holds a B.Tech in Computer Science and a postgraduate diploma in Advanced Computing. She is proficient in various programming languages, databases, and operating systems.
How Accurate is Future Facilities 6Sigma DCXRobert Schmidt
The document discusses the accuracy of the 6SigmaDCX software. It details how Future Facilities has ensured accuracy through an experienced internal development team, engineering validation, and independent audits. The team continuously improves modeling through testing and collaboration with manufacturers. Calibration against real data center measurements has shown results within 1-5% of readings. Independent validation by organizations like Compass Datacenters and Binghamton University found simulated values to closely match practical measurements. This validation ensures 6SigmaDCX maintains the precision required for critical facility decisions.
Visualizing the engineering project lifecycle - Unite CopenhagenUnity Technologies
From design to operations, visualization is a powerful tool to drive informed decision-making on major projects. Point clouds, virtual reality and mobile apps are combining to enable better outcomes throughout the engineering industry. Join this session led by Aurecon to learn how Unity can empower engineers to increase efficiency and realize value through every stage of the project lifecycle.
Speaker: Michael Gardiner - Aurecon
Session available here: https://youtu.be/dixtTbGcCFg
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
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This document discusses supporting architectural variability in software product lines. It begins with an overview of software product lines and product line architectures. It then discusses implementing variability at the architectural level using tactics and design patterns. Tactics aim to achieve certain quality attributes, and there are theories linking concerns to tactics and designs. Feature models can be used to represent relationships between tactics and design alternatives. Tactics can also be implemented using configuration options like in cloud computing platforms. Relating tactics to configuration options allows automatically deriving architectural designs based on quality attribute requirements.
Building product suggestions for a BIM model based on rule sets and a semant...Gonçal Costa Jutglar
The architecture, engineering and construction (AEC) industry today relies on different information systems and computational tools built to support and assist in the building design and construction. However, these systems and tools typically provide this support in isolation from each other. A good combination of these systems and tools is beneficial for a better coordination and information management. Semantic web technologies and a Linked Data approach can be used to fulfil this aim. In this paper, we indicate how these technologies can be applied for one particular objective, namely to check a building information model (BIM) and make suggestions for that model regarding the building elements. These suggestions are based on information obtained from different data sources, including a BIM model, regulations and catalogues of locally available building components.
Meetup 21/9/2017 - Image Recogonition: onmisbaar voor een slimme stad?Digipolis Antwerpen
1) Image recognition and computer vision technologies can enable various smart city applications like crowd behavior analysis, traffic analysis, and thermal signature tracking.
2) Autonomous systems that use computer vision and machine learning can perceive their environment and act independently to help during disasters by providing survivors and emergency personnel with locating information.
3) MATLAB provides tools for computer vision, machine learning, and deep learning that can help develop prototypes and applications for smart cities from idea to product.
Faisal Suleman is seeking a position that allows him to utilize his experience and skills to contribute to a company's growth. He has over 15 years of experience in business intelligence tools such as Siebel Analytics, Oracle OBIEE, and QlikView. He has led several data warehouse projects for companies in industries such as automotive and auction. Faisal has a bachelor's degree in computer science and commerce, and is Oracle Business Intelligence Applications 7 for ERP certified.
This presentation shares his insights on design simulation and the new role of the simulator in Simulator Assisted Engineering. For more information, go to GSES.com, email info@gses.com, or call 800-638-7912. You can also follow GSE at @GSESystems and Facebook.com/GSESystems.
Software Application Presentation SlideLee Pei Gie
The document provides information on various BIM QS computer software including Glodon, CostX, Vico Office, and Nomitech Costos. It discusses the features, functions, and limitations of each software. It also compares BIM, manual measurement, and CAD measurement in terms of benefits and constraints during various project stages from briefing to post-construction. Overall, the document provides a comprehensive overview and comparison of BIM QS software and measurement methods.
1) BIM software provides benefits throughout the project lifecycle from planning to construction and facility management. It allows for improved visualization, coordination, estimation and resource efficiency.
2) However, BIM also faces limitations such as the need for experienced users, high costs, disruption to traditional processes, and challenges with data sharing between stakeholders.
3) While BIM streamlines tasks like quantity take-off, it cannot account for all construction cost variables and may be time consuming for scheduling. Experienced teams are required to leverage its full capabilities.
The document summarizes a presentation given by Carl Collins on March 22nd 2018 about digital engineering and building information modeling (BIM). The presentation covered an introduction to UK BIM standards and requirements, how simulation fits into the BIM process, and key challenges. It discussed how BIM creates a virtual 3D model that is rich in data to inform decisions throughout the project lifecycle. The role of data in the design process was also examined from strategic definition through to building operation.
Jason Chen has over 19 years of experience in analog, digital and mixed-signal CAD development as well as management experience. He has led teams of up to 6 developers in successfully delivering layout verification runs and parameterized cells for over 10 process technologies on a 3-week cycle. As a CAD Engineering Manager, he managed teams that developed physical verification runsets and parasitic extraction runsets to support over 20 process technologies. He has extensive experience in Cadence and Synopsys tools as well as process design kit development and management.
Fiatech 2014 - Big BIM Implementation, Zuhair HaddadCCT International
This document provides details on a big BIM implementation for the Abu Dhabi International Airport Midfield Terminal Building project. It outlines CCC's BIM strategy, which included employing additional BIM engineers well before the project award to allow proper training. CCC utilized established BIM tools and workflows to facilitate integration with project controls. Notable benefits realized through BIM implementation included significant cost and time savings through clash detection and material take-offs, as well as improved construction planning and coordination through use of 4D simulations and a progressive interface model.
The document outlines an ASIC design flow using Mentor Graphics tools and describes 10 experiments for digital and analog circuit design. The design flow involves using Pyxis Schematic for circuit design, Pyxis Layout for layout, and Calibre for verification. The 10 experiments include designing basic gates, adders, latches, counters, static RAM cells, and differential amplifiers. The goal is to take students through the full ASIC design flow from circuit design to tapeout.
The document discusses the development of a computer aided critical lift planning system using parametric modeling software Autodesk Inventor. Existing critical lift planning tools have limitations and do not fully automate the planning process. The system developed in Autodesk Inventor aims to improve on previous tools by incorporating additional features like clearance checking, bearing pressure calculations, and 3D simulation of single and multiple crane lifts. The architecture of the new system is presented, which uses Autodesk Inventor for 3D modeling, Visual Basic for the interface and programming, and Microsoft Access for storing load chart data. The system is meant to make the critical lift planning process more efficient and reliable.
Nesma autumn conference 2015 - Is FPA a valuable addition to predictable agil...Nesma
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Architecture vs. Design in Agile: What’s the Right Answer?TechWell
Is architecture the same as preliminary design in agile? It shouldn't be. Do we create architecture up front, then do iterative development after the architecture is done? That is edging back toward waterfall. Can you explain the purpose of the architecture in just two or three statements? Anthony Crain says that when he asks that question, he gets either verbose answers or blank stares. So Anthony shares an elegantly simple two bullet explanation of what an architecture does. Explore the models architects and designers should produce and learn why these models are so important to keep separate. Understand why it is vital to separate functional from nonfunctional requirements and how this affects architecture, design, and even code and test. Explore what a conceptual architectural model should look like vs. a physical one, and for the conceptual design model vs. a physical one—and the timing of all four models. Finally, examine the impact of iterative development on architecture.
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How do you find the best solution when faced with many choices? Combinatorial optimization is a field of mathematics that seeks to find the most optimal solutions for complex problems involving multiple variables. There are numerous business verticals that can benefit from combinatorial optimization, whether transport, supply chain, or the mobile industry.
More recently, we’ve seen gains from AI for combinatorial optimization, leading to scalability of the method, as well as significant reductions in cost. This method replaces the manual tuning of traditional heuristic approaches with an AI agent that provides a fast metric estimation.
In this presentation you will find out:
Why AI is crucial in combinatorial optimization
How it can be applied to two use cases: improving chip design and hardware-specific compilers
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The document discusses the accuracy of the 6SigmaDCX software. It details how Future Facilities has ensured accuracy through an experienced internal development team, engineering validation, and independent audits. The team continuously improves modeling through testing and collaboration with manufacturers. Calibration against real data center measurements has shown results within 1-5% of readings. Independent validation by organizations like Compass Datacenters and Binghamton University found simulated values to closely match practical measurements. This validation ensures 6SigmaDCX maintains the precision required for critical facility decisions.
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From design to operations, visualization is a powerful tool to drive informed decision-making on major projects. Point clouds, virtual reality and mobile apps are combining to enable better outcomes throughout the engineering industry. Join this session led by Aurecon to learn how Unity can empower engineers to increase efficiency and realize value through every stage of the project lifecycle.
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Session available here: https://youtu.be/dixtTbGcCFg
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The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
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Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
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When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
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Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
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This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
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Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
aziz sancar nobel prize winner: from mardin to nobel
Building Information Modeling Using Constraint Logic Programming
1. Building Information Modeling Using Constraint Logic Programming
Joaquı́n Arias1
Seppo Törmä2
Manuel Carro3,4
Gopal Gupta5
1
CETINIA, Universidad Rey Juan Carlos, Madrid, Spain 2
VisuaLynk Oy, Espoo, Finland
3
Universidad Politécnica de Madrid, Spain 4
IMDEA Software Institute, Pozuelo, Spain
5
University of Texas at Dallas, Richardson, USA
2 August 2022 [ICLP’22]
2. www.ia.urjc.es
Introduction: Modeling BIM models
• Building Information Modeling (BIM) represents the 3D geometry and
properties (costs, materials, process, etc.), of buildings as digital models.
• For each building, architects and engineers create specifics models.
• These specifics models must be shared and combined.
• Automated tools are needed to check the integrity of the merged model.
• In addition, the models must comply with building regulations.
• The design and construction of a building is a sequence of decisions.
Center for Intelligent Information Technologies 1/15
3. www.ia.urjc.es
Introduction: Modeling BIM models
• Building Information Modeling (BIM) represents the 3D geometry and
properties (costs, materials, process, etc.), of buildings as digital models.
• For each building, architects and engineers create specifics models.
• These specifics models must be shared and combined.
• Automated tools are needed to check the integrity of the merged model.
• In addition, the models must comply with building regulations.
• The design and construction of a building is a sequence of decisions.
Automated BIM tools must:
• Combine geometrical reasoning and symbolic/conceptual knowledge.
• Reason in presence of vague concepts and incomplete information.
• Deal with the ambiguity present in regulatory codes and standards.
Center for Intelligent Information Technologies 1/15
4. www.ia.urjc.es
Motivation and Proposal
Logic programming-based tools meet many of the requirements:
• The following examples overcome some limitations of IFC-based tools:
• The query language, BimSPARQL [13].
• Model checkers for safety [14] or acoustic rules [9], and BIMRL [12].
• A translator of building regulation, KBimCode [7].
• A tool based on clingo, ASP4BIM [8].
• However, they have limitations in meeting all requirements.
Center for Intelligent Information Technologies 2/15
5. www.ia.urjc.es
Motivation and Proposal
Logic programming-based tools meet many of the requirements:
• The following examples overcome some limitations of IFC-based tools:
• The query language, BimSPARQL [13].
• Model checkers for safety [14] or acoustic rules [9], and BIMRL [12].
• A translator of building regulation, KBimCode [7].
• A tool based on clingo, ASP4BIM [8].
• However, they have limitations in meeting all requirements.
Our Proposal
• Use tools integrating Constraint Logic Programming with ASP to
model dynamic information and restrictions in BIM models.
• Shift from BIM verification to BIM refinement and to facilitate the
implementation of new specifications, construction standards, etc.
Center for Intelligent Information Technologies 2/15
6. www.ia.urjc.es
Contributions
• A framework, based on Constraint Answer Set Programming (CASP),
that allows unified geometrical and non-geometrical information.
• The prototype of a preliminary 3D reasoner under Prolog with
CLP(Q/R) that we evaluate with several BIM models.
• The outline of an alternative implementation of this spatial reasoner
under CASP, using s(CASP) [2], a goal-directed implementation.
Center for Intelligent Information Technologies 3/15
7. www.ia.urjc.es
Contributions
• A framework, based on Constraint Answer Set Programming (CASP),
that allows unified geometrical and non-geometrical information.
• The prototype of a preliminary 3D reasoner under Prolog with
CLP(Q/R) that we evaluate with several BIM models.
• The outline of an alternative implementation of this spatial reasoner
under CASP, using s(CASP) [2], a goal-directed implementation.
Evidence of advantages of s(CASP) in evaluating BIM models:
• It has the relevance property,
• It can generate justifications for negative queries, and
• It makes representing and reasoning with ambiguities easier.
Center for Intelligent Information Technologies 3/15
8. www.ia.urjc.es
Background: BIM + IFC
• Building information modeling (BIM):
• Combine geometrical information with:
costs, materials, process, etc.
• Allow cost estimations, quantify takeoffs,
energy analysis, etc.
• Goal: achieve consistent of digital models:
• shared with architects, engineers...
• throughout the life cycle of a facility.
• The UK Government requires Level 2 of BIM
maturity for any public project.
• BIM authoring tools: Revit, ArchiCAD, Tekla
Structures, Allplan...
• Common data model: Industry Foundation
Classes (IFC) [4].
Center for Intelligent Information Technologies 4/15
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Background: s(CASP)
• Goal directed execution of Constraint ASP without grounding [2].
• Provide constructive negation, also for constraints:
Center for Intelligent Information Technologies 5/15
10. www.ia.urjc.es
Background: s(CASP)
• Goal directed execution of Constraint ASP without grounding [2].
• Provide constructive negation, also for constraints:
Program
1 size(r1,S):- S #>= 21.
Center for Intelligent Information Technologies 5/15
11. www.ia.urjc.es
Background: s(CASP)
• Goal directed execution of Constraint ASP without grounding [2].
• Provide constructive negation, also for constraints:
Program
1 size(r1,S):- S #>= 21.
Query Model
?- not size(r1,S). { not size(r1,S | {S #< 21})}
Center for Intelligent Information Technologies 5/15
12. www.ia.urjc.es
Background: s(CASP)
• Goal directed execution of Constraint ASP without grounding [2].
• Provide constructive negation, also for constraints:
Program
1 size(r1,S):- S #>= 21.
Query Model
?- not size(r1,S). { not size(r1,S | {S #< 21})}
• ... handle non-stratified negation (generating [partial] models featuring
relevance [10]):
Center for Intelligent Information Technologies 5/15
13. www.ia.urjc.es
Background: s(CASP)
• Goal directed execution of Constraint ASP without grounding [2].
• Provide constructive negation, also for constraints:
Program
1 size(r1,S):- S #>= 21.
Query Model
?- not size(r1,S). { not size(r1,S | {S #< 21})}
• ... handle non-stratified negation (generating [partial] models featuring
relevance [10]):
Program
1 small(r1):- not big(r1).
2 big(r1):- not small(r1).
3 kitchen(r1).
Center for Intelligent Information Technologies 5/15
14. www.ia.urjc.es
Background: s(CASP)
• Goal directed execution of Constraint ASP without grounding [2].
• Provide constructive negation, also for constraints:
Program
1 size(r1,S):- S #>= 21.
Query Model
?- not size(r1,S). { not size(r1,S | {S #< 21})}
• ... handle non-stratified negation (generating [partial] models featuring
relevance [10]):
Program
1 small(r1):- not big(r1).
2 big(r1):- not small(r1).
3 kitchen(r1).
Queries Models
?- small(r1). { small(r1), not big(r1)}
Center for Intelligent Information Technologies 5/15
15. www.ia.urjc.es
Background: s(CASP)
• Goal directed execution of Constraint ASP without grounding [2].
• Provide constructive negation, also for constraints:
Program
1 size(r1,S):- S #>= 21.
Query Model
?- not size(r1,S). { not size(r1,S | {S #< 21})}
• ... handle non-stratified negation (generating [partial] models featuring
relevance [10]):
Program
1 small(r1):- not big(r1).
2 big(r1):- not small(r1).
3 kitchen(r1).
Queries Models
?- small(r1). { small(r1), not big(r1)}
?- big(r1). { big(r1), not small(r1)}
Center for Intelligent Information Technologies 5/15
16. www.ia.urjc.es
Background: s(CASP)
• Goal directed execution of Constraint ASP without grounding [2].
• Provide constructive negation, also for constraints:
Program
1 size(r1,S):- S #>= 21.
Query Model
?- not size(r1,S). { not size(r1,S | {S #< 21})}
• ... handle non-stratified negation (generating [partial] models featuring
relevance [10]):
Program
1 small(r1):- not big(r1).
2 big(r1):- not small(r1).
3 kitchen(r1).
Queries Models
?- small(r1). { small(r1), not big(r1)}
?- big(r1). { big(r1), not small(r1)}
?- kitchen(r1). { kitchen(r1)}
Center for Intelligent Information Technologies 5/15
17. www.ia.urjc.es
Background: s(CASP)
• Goal directed execution of Constraint ASP without grounding [2].
• Provide constructive negation, also for constraints:
Program
1 size(r1,S):- S #>= 21.
Query Model
?- not size(r1,S). { not size(r1,S | {S #< 21})}
• ... handle non-stratified negation (generating [partial] models featuring
relevance [10]):
Program
1 small(r1):- not big(r1).
2 big(r1):- not small(r1).
3 kitchen(r1).
Queries Models
?- small(r1). { small(r1), not big(r1)}
?- big(r1). { big(r1), not small(r1)}
?- kitchen(r1). { kitchen(r1)}
• Support classical negation, i.e., -small(r1) means that we have explicit
evidence that r1 is not small.
Center for Intelligent Information Technologies 5/15
18. www.ia.urjc.es
Background: s(CASP)
• Goal directed execution of Constraint ASP without grounding [2].
• Provide constructive negation, also for constraints:
Program
1 size(r1,S):- S #>= 21.
Query Model
?- not size(r1,S). { not size(r1,S | {S #< 21})}
• ... handle non-stratified negation (generating [partial] models featuring
relevance [10]):
Program
1 small(r1):- not big(r1).
2 big(r1):- not small(r1).
3 kitchen(r1).
Queries Models
?- small(r1). { small(r1), not big(r1)}
?- big(r1). { big(r1), not small(r1)}
?- kitchen(r1). { kitchen(r1)}
• Support classical negation, i.e., -small(r1) means that we have explicit
evidence that r1 is not small.
• s(CASP) generates justification trees (in natural language) [1].
Center for Intelligent Information Technologies 5/15
19. www.ia.urjc.es
Modeling Vague Concepts
• An efficient representation of vague concepts due to unknown
information, ambiguity, and/or administrative discretion, is needed.
Consider the building regulation norm:
In the room there is at least one window, and
each window must be wider than 0.60 m.
If the room is small, it can be between 0.50
and 0.60 m wide.
1 requirement(Room):- not small(Room),
2 window_belongs(Window,Room),
3 width(Window,Width), Width #> 0.60.
4 requirement(Room):- small(Room),
5 window_belongs(Window,Room),
6 width(Window,Width), Width #> 0.50.
• If the size of a room or the criteria to consider that a room is small is
unknown, only the first rule would succeed.
Center for Intelligent Information Technologies 6/15
20. www.ia.urjc.es
Modeling Vague Concepts
• An efficient representation of vague concepts due to unknown
information, ambiguity, and/or administrative discretion, is needed.
Consider the building regulation norm:
In the room there is at least one window, and
each window must be wider than 0.60 m.
If the room is small, it can be between 0.50
and 0.60 m wide.
1 requirement(Room):- not small(Room),
2 window_belongs(Window,Room),
3 width(Window,Width), Width #> 0.60.
4 requirement(Room):- small(Room),
5 window_belongs(Window,Room),
6 width(Window,Width), Width #> 0.50.
• If the size of a room or the criteria to consider that a room is small is
unknown, only the first rule would succeed.
Proposal: Use of stable model semantics [3; 5]
• In the previous example we can consider two scenarios (models):
... in one a given room is small and in the other, it is not.
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Modeling and manipulating 3D objects: CLP(Q/R)
• Convex shapes are represented using linear equations.
• CLP(Q/R) [6] can be used to solve the resulting linear constraints.
• Using CLP(Q/R) objects are represented as a list of convex shapes:
1 box(point(Xa,Ya,Za), point(Xb,Yb,Zb), [convex([X,Y,Z])]) :-
2 X#>=Xa, X#<Xb, Y#>=Ya, Y#<Yb, Z#>=Za, Z#<Zb.
Center for Intelligent Information Technologies 7/15
22. www.ia.urjc.es
Modeling and manipulating 3D objects: CLP(Q/R)
• Convex shapes are represented using linear equations.
• CLP(Q/R) [6] can be used to solve the resulting linear constraints.
• Using CLP(Q/R) objects are represented as a list of convex shapes:
1 box(point(Xa,Ya,Za), point(Xb,Yb,Zb), [convex([X,Y,Z])]) :-
2 X#>=Xa, X#<Xb, Y#>=Ya, Y#<Yb, Z#>=Za, Z#<Zb.
• Operations: union, intersection, complement, and subtraction:
1 obj(r1, [convex([X,Y])]) :- X#>=1, X#<4, Y#>=2, Y#<5.
2 obj(r2, [convex([X,Y])]) :- X#>=3, X#<5, Y#>=1, Y#<4.
?- obj(r1,Sh1), obj(r2,Sh2),sh_intersection(Sh1, Sh2, Int).
Int = [convex([A,B])], A#>=3, A#<4, B#>=2, B#<4 ?
?- obj(r1,Sh1), obj(r2,Sh2),sh_subtraction(Sh1, Sh2, Sub).
Sub = [convex([A,B]),convex([C,D])],
A#>=1,A#<3,B#>=2,B#<5, C#>=3,C#<4,D#>=4,D#<5 ?
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Modeling and manipulating 3D objects: s(CASP) I
• The representation of the convex shapes are part of the program:
1 convex(r1, X, Y) :- X#>=1, X#<4, Y#>=2, Y#<5.
2 convex(r2, X, Y) :- X#>=3, X#<5, Y#>=1, Y#<4.
• They are handled as part of the constraint store of the program.
• A non-convex object is represented with several clauses, one for each
convex shape:
Center for Intelligent Information Technologies 8/15
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Modeling and manipulating 3D objects: s(CASP) I
• The representation of the convex shapes are part of the program:
1 convex(r1, X, Y) :- X#>=1, X#<4, Y#>=2, Y#<5.
2 convex(r2, X, Y) :- X#>=3, X#<5, Y#>=1, Y#<4.
• They are handled as part of the constraint store of the program.
• A non-convex object is represented with several clauses, one for each
convex shape:
?- shape_intersect(r1,r2,Int).
Int = convex([A | { A#>=3, A#<4 }, B | { B#>=2, B#<4 }]) ?
?- shape_subtract(r1,r2,Sub).
Sub = convex([A | { A#>=1, A#<3 }, B | { B#>=2, B#<5 }]) ? ;
Sub = convex([A | { A#>=3, A#<4 }, B | { B#>=4, B#<5 }]) ?
Center for Intelligent Information Technologies 8/15
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Modeling and manipulating 3D objects: s(CASP) II
Operations on a 2D space using s(CASP)
1 % Union = ShA ∪ ShB
2 shape_union(IdA, IdB, convex([X,Y])) :- convex(IdA,X,Y).
3 shape_union(IdA, IdB, convex([X,Y])) :- convex(IdB,X,Y).
4 % Intersection = ShA ∩ ShB
5 shape_intersect(IdA, IdB, convex([X,Y])) :- convex(IdA,X,Y), convex(IdB,X,Y).
6 % Complement = ¬ ShA
7 shape_complement(IdA, convex([X,Y])) :- not convex(IdA,X,Y).
8 % Subtract = ShA ∩ ¬ ShB
9 shape_subtract(IdA, IdB, convex([X,Y])) :- convex(IdA,X,Y), not convex(IdB,X,Y).
• 9 lines of code instead of 39 lines.
• Spatial operations are translated into logical operations.
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(Non)-monotonic iteration in BIM models
• Consider the design process of a room and the fire safety norm below:
• If a gas boiler is used, the ventilation must be natural.
window surface area is at least 10% of the floor area.
• If the boiler is electric, the ventilation could be natural or mechanical.
Center for Intelligent Information Technologies 10/15
27. www.ia.urjc.es
(Non)-monotonic iteration in BIM models
• Consider the design process of a room and the fire safety norm below:
• If a gas boiler is used, the ventilation must be natural.
window surface area is at least 10% of the floor area.
• If the boiler is electric, the ventilation could be natural or mechanical.
• Possible timeline:
1. Initially, the shared BIM model has no
ventilation or boiler restrictions.
2. The architect reduces the size of the
window (surface less that 10%).
3. ALERT: An electric boiler is selected.
4. At the same time, the engineer selects a
gas boiler for efficiency.
5. ALERT: Ventilation must be natural.
6. ERROR: when attempting to merge both
models, an inconsistency is detected.
Center for Intelligent Information Technologies 10/15
28. www.ia.urjc.es
(Non)-monotonic iteration in BIM models
• Consider the design process of a room and the fire safety norm below:
• If a gas boiler is used, the ventilation must be natural.
window surface area is at least 10% of the floor area.
• If the boiler is electric, the ventilation could be natural or mechanical.
• Possible timeline:
1. Initially, the shared BIM model has no
ventilation or boiler restrictions.
2. The architect reduces the size of the
window (surface less that 10%).
3. ALERT: An electric boiler is selected.
4. At the same time, the engineer selects a
gas boiler for efficiency.
5. ALERT: Ventilation must be natural.
6. ERROR: when attempting to merge both
models, an inconsistency is detected.
• A naive approach to handle the ERROR
would broadcast the inconsistency.
Center for Intelligent Information Technologies 10/15
29. www.ia.urjc.es
(Non)-monotonic iteration in BIM models
• Consider the design process of a room and the fire safety norm below:
• If a gas boiler is used, the ventilation must be natural.
window surface area is at least 10% of the floor area.
• If the boiler is electric, the ventilation could be natural or mechanical.
• Possible timeline:
1. Initially, the shared BIM model has no
ventilation or boiler restrictions.
2. The architect reduces the size of the
window (surface less that 10%).
3. ALERT: An electric boiler is selected.
4. At the same time, the engineer selects a
gas boiler for efficiency.
5. ALERT: Ventilation must be natural.
6. ERROR: when attempting to merge both
models, an inconsistency is detected.
• A naive approach to handle the ERROR
would broadcast the inconsistency.
Proposal:
• A continuous integration reasoner:
• that determines who is the expert whose
opinion prevails
• makes a decision based on that.
• notifies the other party to confirm the
adjustments.
Center for Intelligent Information Technologies 10/15
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Evaluation I: Comprehensibility (reason in presence of vague concepts)
• Reason about various scenarios simultaneously (or not).
1 % Hotel with 8 rooms - only the size of 3 of them is known.
2 room(r1). room(r2). room(r3). room(r4).
3 room(r5). room(r6). room(r7). room(r8).
4 size(r1, 25). size(r2, 5). size(r3, 15).
5 % Uncertain whether rooms of 10 to 20 m2 are small or not.
6 evidence(Room, small) :- size(Room,Size), Size#<10.
7 -evidence(Room, small) :- size(Room,Size), Size#>20.
8 % Explicit evidence for / against or generate two models.
9 small(Room) :- evidence(Room,small).
10 -small(Room) :- -evidence(Room,small).
11 small(Room) :- not evidence(Room,small), not -small(Room).
12 -small(Room) :- not -evidence(Room,small), not small(Room).
13 % Inferring conclusions from evidence and/or assumptions.
14 room_is(Room,big) :- room(Room), -small(Room).
15 room_is(Room,small) :- room(Room), small(Room).
Center for Intelligent Information Technologies 12/15
32. www.ia.urjc.es
Evaluation I: Comprehensibility (reason in presence of vague concepts)
• Reason about various scenarios simultaneously (or not).
1 % Hotel with 8 rooms - only the size of 3 of them is known.
2 room(r1). room(r2). room(r3). room(r4).
3 room(r5). room(r6). room(r7). room(r8).
4 size(r1, 25). size(r2, 5). size(r3, 15).
5 % Uncertain whether rooms of 10 to 20 m2 are small or not.
6 evidence(Room, small) :- size(Room,Size), Size#<10.
7 -evidence(Room, small) :- size(Room,Size), Size#>20.
8 % Explicit evidence for / against or generate two models.
9 small(Room) :- evidence(Room,small).
10 -small(Room) :- -evidence(Room,small).
11 small(Room) :- not evidence(Room,small), not -small(Room).
12 -small(Room) :- not -evidence(Room,small), not small(Room).
13 % Inferring conclusions from evidence and/or assumptions.
14 room_is(Room,big) :- room(Room), -small(Room).
15 room_is(Room,small) :- room(Room), small(Room).
• For ?- room_is(Room,Size):
• s(CASP) returns 14 partial
models.
• clingo returns 64 models.
Center for Intelligent Information Technologies 12/15
33. www.ia.urjc.es
Evaluation I: Comprehensibility (reason in presence of vague concepts)
• Reason about various scenarios simultaneously (or not).
1 % Hotel with 8 rooms - only the size of 3 of them is known.
2 room(r1). room(r2). room(r3). room(r4).
3 room(r5). room(r6). room(r7). room(r8).
4 size(r1, 25). size(r2, 5). size(r3, 15).
5 % Uncertain whether rooms of 10 to 20 m2 are small or not.
6 evidence(Room, small) :- size(Room,Size), Size#<10.
7 -evidence(Room, small) :- size(Room,Size), Size#>20.
8 % Explicit evidence for / against or generate two models.
9 small(Room) :- evidence(Room,small).
10 -small(Room) :- -evidence(Room,small).
11 small(Room) :- not evidence(Room,small), not -small(Room).
12 -small(Room) :- not -evidence(Room,small), not small(Room).
13 % Inferring conclusions from evidence and/or assumptions.
14 room_is(Room,big) :- room(Room), -small(Room).
15 room_is(Room,small) :- room(Room), small(Room).
• For ?- room_is(Room,Size):
• s(CASP) returns 14 partial
models.
• clingo returns 64 models.
Considering 16 rooms
• s(CASP) returns 30 partial models.
• clingo returns 16384 models.
Center for Intelligent Information Technologies 12/15
34. www.ia.urjc.es
Evaluation I: Comprehensibility (reason in presence of vague concepts)
• Reason about various scenarios simultaneously (or not).
1 % Hotel with 8 rooms - only the size of 3 of them is known.
2 room(r1). room(r2). room(r3). room(r4).
3 room(r5). room(r6). room(r7). room(r8).
4 size(r1, 25). size(r2, 5). size(r3, 15).
5 % Uncertain whether rooms of 10 to 20 m2 are small or not.
6 evidence(Room, small) :- size(Room,Size), Size#<10.
7 -evidence(Room, small) :- size(Room,Size), Size#>20.
8 % Explicit evidence for / against or generate two models.
9 small(Room) :- evidence(Room,small).
10 -small(Room) :- -evidence(Room,small).
11 small(Room) :- not evidence(Room,small), not -small(Room).
12 -small(Room) :- not -evidence(Room,small), not small(Room).
13 % Inferring conclusions from evidence and/or assumptions.
14 room_is(Room,big) :- room(Room), -small(Room).
15 room_is(Room,small) :- room(Room), small(Room).
• For ?- room_is(Room,Size):
• s(CASP) returns 14 partial
models.
• clingo returns 64 models.
Considering 16 rooms
• s(CASP) returns 30 partial models.
• clingo returns 16384 models.
This exponential explosion in the
# models generated by clingo...
... reduces the comprehensibility.
Center for Intelligent Information Technologies 12/15
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Evaluation II: Geometrical and non-geometrical information.
(a) Duplex_Q1.html (b) Duplex_Q2.html
(c) Office_Q1.html (d) Office_Q2.html
Query Duplex Q1: The doors are in green and the rest of the objects are in blue.
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Evaluation II: Geometrical and non-geometrical information.
(a) Duplex_Q1.html (b) Duplex_Q2.html
(c) Office_Q1.html (d) Office_Q2.html
Query Duplex Q2: imposes the constraints Ya#<-4 to select certain doors, and Y#>=-7, Y#<-4
to create a space (unbounded in the axis x and z) that defines a slice of the model.
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Evaluation II: Geometrical and non-geometrical information.
(a) Duplex_Q1.html (b) Duplex_Q2.html
(c) Office_Q1.html (d) Office_Q2.html
Constraints can be used in s(CASP) to reason about unbounded spaces
• Finer constraints, such as Ya#<-4.002, can be used without performance impact.
• That is in general not the case with other ASP systems.
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Evaluation II: Geometrical and non-geometrical information.
(a) Duplex_Q1.html (b) Duplex_Q2.html
(c) Office_Q1.html (d) Office_Q2.html
Query Office Q1/Q2: selects objects of type IfcBeam in the Architecture model that are not
covered by objects in the Structural BIM model.
(c) shows the objects that intersect the beam. (d) shows the uncovered parts drawn in red.
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Evaluation II: Geometrical and non-geometrical information.
(a) Duplex_Q1.html (b) Duplex_Q2.html
(c) Office_Q1.html (d) Office_Q2.html
Performance Query Office Q1/Q2
• Finds the first beam with uncovered parts in only 0.104 sec.
• Selects 691 beams out of 3639 objects in the architecture model and detected 511 beams
not covered by the more than 1300 objects in the structure model in 48 sec.
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42. www.ia.urjc.es
Conclusions
• We have highlighted the advantages of a well-founded approach to:
• Represent geometrical and non-geometrical building information
(including specifications, codes, and/or guidelines) as digital models.
• Handle changes to the models during their design, construction, and/or
facility time (removing, adding, or changing objects and properties).
• The use of CLP, and more specifically s(CASP), makes it possible to:
• Realize commonsense reasoning including geometrical data.
• Represent knowledge involving vague and/or unknown information.
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43. www.ia.urjc.es
Conclusions
• We have highlighted the advantages of a well-founded approach to:
• Represent geometrical and non-geometrical building information
(including specifications, codes, and/or guidelines) as digital models.
• Handle changes to the models during their design, construction, and/or
facility time (removing, adding, or changing objects and properties).
• The use of CLP, and more specifically s(CASP), makes it possible to:
• Realize commonsense reasoning including geometrical data.
• Represent knowledge involving vague and/or unknown information.
Future work
• Shift from BIM verification to BIM refinement.
• Develop non-monotonic model refinement methods.
• Integrate logical reasoning in BIM Software.
Center for Intelligent Information Technologies 15/15
44. www.ia.urjc.es
Conclusions
• We have highlighted the advantages of a well-founded approach to:
• Represent geometrical and non-geometrical building information
(including specifications, codes, and/or guidelines) as digital models.
• Handle changes to the models during their design, construction, and/or
facility time (removing, adding, or changing objects and properties).
• The use of CLP, and more specifically s(CASP), makes it possible to:
• Realize commonsense reasoning including geometrical data.
• Represent knowledge involving vague and/or unknown information.
Future work
• Shift from BIM verification to BIM refinement.
• Develop non-monotonic model refinement methods.
• Integrate logical reasoning in BIM Software. THANKS!
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45. References www.ia.urjc.es
Referencias I
[1] J. Arias, M. Carro, Z. Chen, and G. Gupta (2020). Justifications for Goal-Directed
Constraint Answer Set Programming. In: Proceedings 36th International Conference on
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Association, pp. 59–72. doi: 10.4204/EPTCS.325.12.
[2] J. Arias, M. Carro, E. Salazar, K. Marple, and G. Gupta (2018). Constraint Answer Set
Programming without Grounding. In: Theory and Practice of Logic Programming 18.3-4,
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[3] J. Arias, M. Moreno-Rebato, J. A. Rodriguez-Garcı́a, and S. Ossowski (2021). Modeling
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[4] BuildingSMART (2020). Industry Foundation Classes (IFC). Available at:
https://technical.buildingsmart.org/standards/ifc/. Accessed on July, 2020.
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46. References www.ia.urjc.es
Referencias II
[5] M. Gelfond and V. Lifschitz (1988). The Stable Model Semantics for Logic Programming.
In: ICLP’88, pp. 1070–1080.
[6] C. Holzbaur (1995). OFAI CLP(Q,R) Manual, Edition 1.3.3. Tech. rep. TR-95-09. Vienna:
Austrian Research Institute for Artificial Intelligence.
[7] H. Lee, J. Lee, S. Park, and I. Kim (2016). Translating building legislation into a
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Principles and Practice of Declarative Programming, pp. 1–12.
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47. References www.ia.urjc.es
Referencias III
[9] P. Pauwels, D. Van Deursen, R. Verstraeten, J. De Roo, R. De Meyer, R. Van De Walle, and
J. Van Campenhout (2011). A semantic rule checking environment for building
performance checking. en. In: Automation in Construction 20.5, pp. 506–518. doi:
10.1016/j.autcon.2010.11.017.
[10] L. M. Pereira and J. N. Aparı́cio (1989). Relevant Counterfactuals. In: EPIA 89, 4th
Portuguese Conference on Artificial Intelligence, Lisbon, Portugal, September 26-29, 1989,
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[11] Singapore Government (2016). Corenet BIM e-Submission.
https://www.corenet.gov.sg/general/building-information-modeling-(bim)-e-
submission.aspx.
[12] W. Solihin (2015). A simplified BIM data representation using a relational database
schema for an efficient rule checking system and its associated rule checking
language. PhD thesis. Georgia Institute of Technology.
Center for Intelligent Information Technologies 18/15
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Referencias IV
[13] C. Zhang, J. Beetz, and B. de Vries (2018). BimSPARQL: Domain-specific functional
SPARQL extensions for querying RDF building data. In: Semantic Web 9.6,
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[14] S. Zhang, J. Teizer, J. Lee, C. Eastman, and M. Venugopal (2013). Building Information
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and Schedules. en. In: Automation in Construction 29, pp. 183–195. doi:
10.1016/j.autcon.2012.05.006.
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