A Decision Support System (DSS) for constructability assessment in seismic retrofit of complex buildings

Choosing the optimal strategy for the seismic retrofit of an existing building is a difficult problem. This difficulty increases in the case of complex buildings systems with different strategic requirements in terms of organization layout and structural features. This paper contributes to solving this complexity by combining management and technical strategies, especially in situations of comparable times and costs. It is demonstrated that the best way to obtain final results that are consistent with the initial requirements is to intervene at the beginning of the design stage. To this end the implementation of a Decision Support System (DSS) aided by Information Technology (IT) is presented for making a constructability assessment of the seismic retrofit of complex buildings. Different seismic retrofit scenarios compete to be the optimal retrofit solution. Several evaluation systems are combined with classic constructability-based tools to produce an organic framework. A rule-based engine that utilizes this framework can be implemented on top of user-friendly software. The DSS intends to control building management by prefiguring a real ongoing building execution after the early stages of the project. This is made possible by using the simulation of site safety layout in all compatible scenarios. By managing the output data of IT models it is possible to assess both management and structural strategies. In the end the DSS combines them to choose the most favorable overall solution. Looking towards future development, it can be seen that applications of a BIM Platform integrated with the proposed DSS have considerable potential in construction management practice

Ontologies for Knowledge modeling in construction planning

Nowadays, there is an increasing recognition of the value of knowledge management in the construction projects and ontology-based semantic modelling is seen as an important means of addressing this problem, even if a knowledge-base which maps the construction planning and scheduling domains, in a formal and machine-readable way, is still missing. Addressing this issue, the book is divided in two parts. Part I, theory, is a theoretical introduction of on ontologies concepts and expert systems. Part II, application, presents a research of ontologies development for semantic modelling of construction scheduling, workspace, product and time domains. The last chapter presents the architecture of an ontology-based expert system, to show how ontologies can support automated planning mechanisms

Ein BIM Ontologie-basiertes Expertensystem für räumliche und zeitliche Programmierungen von Bauten

The effective realization of building construction is closely linked to the construction schedules that, if poorly designed, result in congested site areas, accidents and decline of productivity. In the past decade, many research efforts have been spent in BIM which represents the process of preparation and use of a computer-generated Building Information Model (BIM) even if an effective model to assist construction scheduling is still missing. This PhD thesis proposes an Expert-System able to identify the shortest completion sequence of a given Building Information Model, considering the on-site temporal-space allocation of workspaces. It is supported by an ontology-based system architecture integrated with a rule-based artificial intelligence. Four integrated ontologies, to formally represent construction site entities, constitute the system’s Knowledge-Base (KB): (1) scheduling ontology that maps the necessary components to specify the scheduling task (2) space ontology that contains workspaces requirements in terms of geometries, locations and interactions (3) products ontology that describes geometrical and topological information of the building objects (4) time ontology that describes temporal properties of site entities in their evolution across time. Such a KB was rendered into a Protégé’s script (ontology editing environment) in order to convert it in machine-readable language (i.e., Web Ontology Language –OWL). Furthermore, four automated Reasoning Mechanisms –scripts- were incorporated in the model architecture: (i) an algorithm to define the on-site workspaces configuration pattern, (ii) an algorithm to automatically model workspaces geometries, (iii) a workspaces conflicts checking process and (iv) a rule-engine to deduce the shortest construction sequence and solve the identified conflicts manipulating the KB itself. A validation test was conducted on a BIM-based project of an industrial building composed of 98 building items and 611 workspaces, allocated by means of (i) and modelled with (ii). A construction sequence of 36 construction days was suggested by the system. Moreover, 118 workspaces conflicts were identified (iii) and automatically solved by using the planning rules included in the rule-engine as it was visually verified simulating the sequence itself within a 4D-BIM environment. This prototype can be considered a precursor model in developing BIM-based intelligent systems architectures for spatial construction planning.Eine erfolgreiche Umsetzung eines Gebäudeprojektes ist von der Planung der Montage auf der Baustelle abhängig. Im letzten Jahrzehnt wurden zahlreiche wissenschaftliche Projekte zur Montageplanung unter Verwendung eines Computermodells im Rahmen des Building Information Modelling (BIM) durchgeführt. Momentan fehlt aber noch ein Modell, das auch den Prozess selber auf der Baustelle integriert. In der vorliegenden Arbeit wird ein Expertensystem mit dem Ziel der Findung einer optimalen Montagefolge vorgestellt. Das Expertensystem basiert auf BIM und berücksichtigt die räumliche und zeitliche Interaktion der Arbeitsabläufe auf der Baustelle. Die entwickelte Methode stützt sich auf einer Ontologie-basierten Architektur, die in einer Regel-basierten künstlichen Intelligenz integriert ist. Dabei wird ein neues Objekt in das BIM Modell eingefügt, das den Raumbedarf einer Montagetätigkeit beschreibt. Dies kann beispielsweise ein erforderlicher Freiraum für einen Mobilkran sein oder ein bei der Montage nicht betretbarer Sicherheitsbereich. Die Wissensbasis des Expertensystems besteht aus vier Ontologien, die nötig sind um das Wesen der Baustelle darzustellen: Ontologie der Montageabläufe, die den technischen Ablauf der Aktivitäten bestimmt; Ontologie der baulichen Räume, die den räumlichen Bedarf berücksichtigt; Ontologie der Elemente des Gebäudes, welche die geometrischen und funktionalen Gebäudeelemente beschreibt, um Arbeitsprozesse zu bestimmen; Ontologie der Zeit, welche die Reihenfolge der Bauelemente vorgibt. Die Wissensbasis ist mit einem Protégé-Skript als Ontologie-Editor entwickelt worden, für einen Compiler der Web Ontology Language (OWL). Danach wurde die Wissensbasis mit vier Algorithmen verknüpft: Ein Algorithmus, der den Arbeitsraum definiert; Ein Algorithmus, der die Geometrien der Arbeitsräume modelliert; Ein Kontrollprozess, der die Konfliktstellen des Arbeitsraum identifiziert; Ein Optimierungs-Prozess, der den kürzesten Arbeitsprozess ermittelt. Zur Validierung wurde ein Industriegebäude mit 98 Elementen verwendet. Das Expertensystem hatte 611 Arbeitsräume errechnet und eine geschätzte Bauzeit von 36 Tagen. Das Expertensystem identifizierte 118 Konfliktstellen und entwickelte jeweils Lösungen. Das Ergebnis wurde mit Hilfe einer 4D-BIM Umgebung visualisiert. Das vorgestellte Expertensystem ist ein Prototyp, der einen Beitrag zur Entwicklung automatischer und intelligenter Programmierungen für den Montageablauf unter Verwendung von BIM leistet

On-demand generation of as-built infrastructure information models for mechanised tunnelling from TBM data: a computational design approach

When dealing with complex curved geometries and massive datasets linked to linear infrastructures, manual generation and maintenance of related multidisciplinary BIM models and documentation are yet to be fully automated. This research focused on the integration of BIM and computational design into an intuitive approach for the generation of as-built models of mechanised tunnelling projects, leveraging the use of the real-time data collected by TBM. A preliminary study of the parameters was conducted to describe the curved geometry of the tunnel, then their mutual alignment was followed by the identification of additional relevant information. To automate the tunnel's BIM-based modelling process, a system of four coupled algorithms was developed and tested with a sample TBM dataset. The results demonstrate how the adoption of computational design methods drastically enhances the modelling process for infrastructure projects, allowing for the on-demand generation of as-built BIM models, reducing time and errors

Evaluation of Immersive VR Experiences for Safety Training of Construction Workers: A Semi-Qualitative Approach Proposal

The diffusion of Building Information Modeling (BIM) and advanced visualization technologies in the increasingly digitalised construction sector is fostering the development and implementation of disruptive approaches for workforce Health and Safety (H&S) training. Project-specific risks, safety procedures and information can be administered through immersive Virtual Reality (VR) experiences where construction site environments and activities are reproduced without exposing the trainees to real hazards. However, despite numerous research and industry applications demonstrating the potential benefits of these technologies, a standardized framework and methodology for the evaluation of VR safety training effectiveness for construction workers is still lacking hence hindering its large scale-adoption and recognition from policymakers. Within the scope of previous authors contributions on the development and implementation of BIM-based VR experiences for construction workers’ safety training, this paper aims to address the evaluation of their effectiveness proposing a novel semi-qualitative approach based on the integration of trainees’ subjective and objective data. A postexperience evaluation questionnaire is developed to collect trainees’ direct and qualitative feedback about the experience immersivity and perceived safety content transfer. Furthermore, the integration with trainees’ spatial tracking data is proposed to complement the qualitative feedback with the quantitative evaluation of their use of the virtual space for safety training purposes. The application of the presented approach in case study is currently undergoing and the related results will be subject of future contributions

A Semantic Digital Twin Prototype for Workplace Performance Assessment

Nowadays, despite the growing attention to indoor environmental quality and comfort, existing workplaces still often fail to meet employees’ expectations and needs, affecting their well-being and productivity. In order to improve management decisions, crucial insights can be provided by the timely correlation of objective workplace conditions, observed by sensors, and subjective workers’ feedback, collected through Ecological Momentary Assessment (EMA) method. This paper presents a prototypical Digital Twin for the assessment of workplace performance from an occupant-centric perspective, based on the integration of IoT, BIM and Semantic Web technologies. Following the definition of relevant use cases and requirements a layered system architecture is presented and the prototype implementation is discussed. For capturing the workplace’s environmental properties, a sensor network based on the Zigbee communication standard is proposed due to its data transmission efficiency. The measured data, converted in the lightweight MQTT protocol, are streamed to an InfluxDB time series database where they are stored along with the incoming workers’ feedback collected as survey responses with a dedicated web application. These time series data are queried and transported into a developed web platform for integrating BIM and RDF data within the standardized structure of Information Containers for linked Document Delivery (ICDDs). Inside this platform, the IFC model of the workplace, the measured data from the sensors, and the worker generated RDF data according to the WOMO ontology for occupant-centric workplace management are linked. The capabilities of the workplace Digital Twin prototype are finally demonstrated querying the linked heterogeneous data to fulfil workplace management tasks in a case study provided at the end of the pape

BIM-based Code Checking for Construction Health and Safety

The research project aims to define an H&S BIM-based design and validation workflow, specifying the minimum level of requirements and mandatory informative content for the submission of construction site layouts and safety plans. The paper is focused on the translation into a parametric rule-set of the Italian construction sites’ H&S normative text (D.Lgs. 81/2008). A semantic analysis was used in order to translate it into computable parameters to be implemented into checking rules. Object tables have been created for each construction site element regulated by the D.Lgs. 81/2008. Based on those tables, meant as guideline for the design phase, a BIM library for the construction site has been created and a model checking tool has been used for creating rules to check and validate BIM objects and mutual relations