{"id":{"repo_id":"tu-berlin","oai_identifier":"oai:depositonce.tu-berlin.de:11303/24944"},"canonical_url":"https://search.dev.ndltd.org/etd/tu-berlin/oai:depositonce.tu-berlin.de:11303/24944","repository":{"repo_id":"tu-berlin","name":"Technische Universität Berlin","base_url":"https://api-depositonce.tu-berlin.de/server/oai/request"},"display":{"title":"Exploring construction process simulation as part of the digital twin concept in real-world contexts","abstract":"The construction industry faces problems like low productivity, poor resource efficiency, and cost overruns. Several factors cause these problems. Each building is a unique product, constructed by changing teams on temporary sites under constantly changing conditions. Although operating sequences may be similar across projects, the unique aspects of each construction project make planning complex. Therefore, each project needs adaptable planning to respond to specific conditions. However, construction management usually relies on generic information based on assumptions or historical data and inflexible planning methods. These generic approaches cannot reflect the inherent dynamic processes of construction works and hinder successful management. With advancing digitalisation, there is potential to change conventional practices. Methods like discrete event simulation can help manage construction processes by providing insights regarding future developments. However, reliable information regarding the current state of the system is essential for effective use. The digital twin concept aims to continuously collect and analyse data to provide context-specific information. Therefore, the digital twin concept can significantly improve simulation and management practices in the construction industry. This dissertation explores how the digital twin approach can be integrated with process simulation to optimise construction management. We propose a framework to establish a systematic flow of real-time data. This framework includes three advanced analytics-based methods to convert raw data into quantitative key performance indicators. These methods enable the analysis of real-time data, simulation calibration, and simulation-driven decision support. The applicability of each method was validated in a real-world case study to provide evidence for the methods' usage. Activity durations are the most important factor for construction planning. Thus, the lack of reliable activity durations is a significant shortcoming in management practice. Due to advancing digitalisation, data can be collected to track construction progress on-site in real-time, e.g., by smart sensors. The analysis of data collected during construction enables the extraction of data-based activity durations. Previous approaches for data-based extractions of activity durations focused on cycle times or mean durations. However, cycle times and mean durations are simplifications as valuable information can get lost. To this end, in Chapter 2, we propose a method to extract individual activity durations for each repetition. The method applies machine learning to classify in real time collected kinematic data to recognise activities and extract durations automatically. Subsequently, automatic post-processing using logical rules identifies and corrects misclassifications to improve the accuracy of duration extractions. We illustrated the applicability of the method in a case study for the work process of concreting with a tower crane, which consists of four consecutive activities. The case study demonstrated the successful applicability of the method by comparing the data-based and the measured durations. In the case study, the method disproved normality for each of the four sets of durations. Thus, the results emphasise the need to extract the durations of individual activity repetitions. The automated provision of project-specific activity durations provides management with meaningful and reliable information on construction progress. The provision of reliable activity durations enables accurate forecasts and, thus, supports the implementation of simulation in construction planning. By using updated durations, simulation models can be continuously calibrated. However, simulation practice often misses continuous model calibration and neglects real-world dynamics and uncertainties in forecasts. Chapter 3 addresses these issues by developing a method for automated and continuous discrete event simulation calibrations. Based on sets of durations, the method uses stochastic productivity modelling to identify the most suitable probability density functions representing productivity rates. The method uses the identified functions to continuously calibrate a simulation model to reflect the changing conditions of the system. The method validates functions and the simulation model through statistical tests. We illustrated the application of the method in a case study of concrete pouring using a tower crane. The results of the case study revealed that validation of input parameters, i.e. probability density functions, does not necessarily entail validated simulation models. The calibration method facilitates in-depth analysis of how changes in productivity rates affect simulation forecasts aiming to build reliable models. Once reliable models are available, managers can use simulations to assess how different decisions affect construction outcomes. However, simulation modelling of different options is often complex, laborious, and time-consuming. Therefore, managers rarely use simulation for planning and the possible impacts of different management decisions are unknown. To this end, in Chapter 4, we propose a simulation-driven decision support method to optimise on-site construction process management. Building on the previous chapters, Chapter 4 presents the entire envisioned system for applying simulation as part of the digital twin concept by an integrated method. The simulation-driven method automatically models many options based on inputs for resource allocations, delivery scenarios, and constraint settings. The method applies stochastic discrete event simulation to perform a scenario analyses considering uncertainties when calculating key performance indicators. Additionally, the method performs multi-criteria optimisation to identify Pareto-optimal options. A real-world case study involving the construction of an industrial building validated the method. The results revealed that an option that performs best regarding the mean value can underperform in extreme scenarios, such as the best-case. These insights underscore the need for stochastic investigations when using simulation for decision support. The simulation-driven method simplifies modelling and gives managers a comprehensive understanding of potential outcomes to promote successful construction. This dissertation makes several significant contributions. It provides a systematic approach to integrating real-time data with construction simulations. We developed three advanced analytics methods for automated, continuous, and comprehensive processing of real-time data. The methods enable a data stream to convert collected kinematic data into key performance indicators. By using these methods, managers can gain meaningful insights into construction progress, develop reliable simulation models, and better understand the impacts of decisions before implementation. We validated each method and the entire integrated approach through real-world case studies to provide evidence for the potential of simulation as part of the digital twin concept. The proposed approach aims at iterative management that follows the Plan-Do-Study-Act cycle to support decision-making proactively. Our approach offers significant potential to implement data-driven decision-making and improve performance in the construction industry.","abstract_html":"The construction industry faces problems like low productivity, poor resource efficiency, and cost overruns. Several factors cause these problems. Each building is a unique product, constructed by changing teams on temporary sites under constantly changing conditions. Although operating sequences may be similar across projects, the unique aspects of each construction project make planning complex. Therefore, each project needs adaptable planning to respond to specific conditions. However, construction management usually relies on generic information based on assumptions or historical data and inflexible planning methods. These generic approaches cannot reflect the inherent dynamic processes of construction works and hinder successful management. With advancing digitalisation, there is potential to change conventional practices. Methods like discrete event simulation can help manage construction processes by providing insights regarding future developments. However, reliable information regarding the current state of the system is essential for effective use. The digital twin concept aims to continuously collect and analyse data to provide context-specific information. Therefore, the digital twin concept can significantly improve simulation and management practices in the construction industry. This dissertation explores how the digital twin approach can be integrated with process simulation to optimise construction management. We propose a framework to establish a systematic flow of real-time data. This framework includes three advanced analytics-based methods to convert raw data into quantitative key performance indicators. These methods enable the analysis of real-time data, simulation calibration, and simulation-driven decision support. The applicability of each method was validated in a real-world case study to provide evidence for the methods' usage. Activity durations are the most important factor for construction planning. Thus, the lack of reliable activity durations is a significant shortcoming in management practice. Due to advancing digitalisation, data can be collected to track construction progress on-site in real-time, e.g., by smart sensors. The analysis of data collected during construction enables the extraction of data-based activity durations. Previous approaches for data-based extractions of activity durations focused on cycle times or mean durations. However, cycle times and mean durations are simplifications as valuable information can get lost. To this end, in Chapter 2, we propose a method to extract individual activity durations for each repetition. The method applies machine learning to classify in real time collected kinematic data to recognise activities and extract durations automatically. Subsequently, automatic post-processing using logical rules identifies and corrects misclassifications to improve the accuracy of duration extractions. We illustrated the applicability of the method in a case study for the work process of concreting with a tower crane, which consists of four consecutive activities. The case study demonstrated the successful applicability of the method by comparing the data-based and the measured durations. In the case study, the method disproved normality for each of the four sets of durations. Thus, the results emphasise the need to extract the durations of individual activity repetitions. The automated provision of project-specific activity durations provides management with meaningful and reliable information on construction progress. The provision of reliable activity durations enables accurate forecasts and, thus, supports the implementation of simulation in construction planning. By using updated durations, simulation models can be continuously calibrated. However, simulation practice often misses continuous model calibration and neglects real-world dynamics and uncertainties in forecasts. Chapter 3 addresses these issues by developing a method for automated and continuous discrete event simulation calibrations. Based on sets of durations, the method uses stochastic productivity modelling to identify the most suitable probability density functions representing productivity rates. The method uses the identified functions to continuously calibrate a simulation model to reflect the changing conditions of the system. The method validates functions and the simulation model through statistical tests. We illustrated the application of the method in a case study of concrete pouring using a tower crane. The results of the case study revealed that validation of input parameters, i.e. probability density functions, does not necessarily entail validated simulation models. The calibration method facilitates in-depth analysis of how changes in productivity rates affect simulation forecasts aiming to build reliable models. Once reliable models are available, managers can use simulations to assess how different decisions affect construction outcomes. However, simulation modelling of different options is often complex, laborious, and time-consuming. Therefore, managers rarely use simulation for planning and the possible impacts of different management decisions are unknown. To this end, in Chapter 4, we propose a simulation-driven decision support method to optimise on-site construction process management. Building on the previous chapters, Chapter 4 presents the entire envisioned system for applying simulation as part of the digital twin concept by an integrated method. The simulation-driven method automatically models many options based on inputs for resource allocations, delivery scenarios, and constraint settings. The method applies stochastic discrete event simulation to perform a scenario analyses considering uncertainties when calculating key performance indicators. Additionally, the method performs multi-criteria optimisation to identify Pareto-optimal options. A real-world case study involving the construction of an industrial building validated the method. The results revealed that an option that performs best regarding the mean value can underperform in extreme scenarios, such as the best-case. These insights underscore the need for stochastic investigations when using simulation for decision support. The simulation-driven method simplifies modelling and gives managers a comprehensive understanding of potential outcomes to promote successful construction. This dissertation makes several significant contributions. It provides a systematic approach to integrating real-time data with construction simulations. We developed three advanced analytics methods for automated, continuous, and comprehensive processing of real-time data. The methods enable a data stream to convert collected kinematic data into key performance indicators. By using these methods, managers can gain meaningful insights into construction progress, develop reliable simulation models, and better understand the impacts of decisions before implementation. We validated each method and the entire integrated approach through real-world case studies to provide evidence for the potential of simulation as part of the digital twin concept. The proposed approach aims at iterative management that follows the Plan-Do-Study-Act cycle to support decision-making proactively. Our approach offers significant potential to implement data-driven decision-making and improve performance in the construction industry.","abstract_has_math":false,"creators":["Jungmann, Manuel"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Hartmann, Timo"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-27T21:28:35Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":["https://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doi.org/10.14279/depositonce-23760"],"render_values":[{"text":"https://doi.org/10.14279/depositonce-23760","href":"https://doi.org/10.14279/depositonce-23760","code":true}]}]},"links":{"outbound_url":"https://depositonce.tu-berlin.de/handle/11303/24944","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Hartmann, Timo"]},{"key":"dc:creator","label":"Author","values":["Jungmann, Manuel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-06-11T08:21:37Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-06-11T08:21:37Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://depositonce.tu-berlin.de/handle/11303/24944","https://doi.org/10.14279/depositonce-23760"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The construction industry faces problems like low productivity, poor resource efficiency, and cost overruns. Several factors cause these problems. Each building is a unique product, constructed by changing teams on temporary sites under constantly changing conditions. Although operating sequences may be similar across projects, the unique aspects of each construction project make planning complex. Therefore, each project needs adaptable planning to respond to specific conditions. However, construction management usually relies on generic information based on assumptions or historical data and inflexible planning methods. These generic approaches cannot reflect the inherent dynamic processes of construction works and hinder successful management. With advancing digitalisation, there is potential to change conventional practices. Methods like discrete event simulation can help manage construction processes by providing insights regarding future developments. However, reliable information regarding the current state of the system is essential for effective use. The digital twin concept aims to continuously collect and analyse data to provide context-specific information. Therefore, the digital twin concept can significantly improve simulation and management practices in the construction industry. This dissertation explores how the digital twin approach can be integrated with process simulation to optimise construction management. We propose a framework to establish a systematic flow of real-time data. This framework includes three advanced analytics-based methods to convert raw data into quantitative key performance indicators. These methods enable the analysis of real-time data, simulation calibration, and simulation-driven decision support. The applicability of each method was validated in a real-world case study to provide evidence for the methods' usage. Activity durations are the most important factor for construction planning. Thus, the lack of reliable activity durations is a significant shortcoming in management practice. Due to advancing digitalisation, data can be collected to track construction progress on-site in real-time, e.g., by smart sensors. The analysis of data collected during construction enables the extraction of data-based activity durations. Previous approaches for data-based extractions of activity durations focused on cycle times or mean durations. However, cycle times and mean durations are simplifications as valuable information can get lost. To this end, in Chapter 2, we propose a method to extract individual activity durations for each repetition. The method applies machine learning to classify in real time collected kinematic data to recognise activities and extract durations automatically. Subsequently, automatic post-processing using logical rules identifies and corrects misclassifications to improve the accuracy of duration extractions. We illustrated the applicability of the method in a case study for the work process of concreting with a tower crane, which consists of four consecutive activities. The case study demonstrated the successful applicability of the method by comparing the data-based and the measured durations. In the case study, the method disproved normality for each of the four sets of durations. Thus, the results emphasise the need to extract the durations of individual activity repetitions. The automated provision of project-specific activity durations provides management with meaningful and reliable information on construction progress. The provision of reliable activity durations enables accurate forecasts and, thus, supports the implementation of simulation in construction planning. By using updated durations, simulation models can be continuously calibrated. However, simulation practice often misses continuous model calibration and neglects real-world dynamics and uncertainties in forecasts. Chapter 3 addresses these issues by developing a method for automated and continuous discrete event simulation calibrations. Based on sets of durations, the method uses stochastic productivity modelling to identify the most suitable probability density functions representing productivity rates. The method uses the identified functions to continuously calibrate a simulation model to reflect the changing conditions of the system. The method validates functions and the simulation model through statistical tests. We illustrated the application of the method in a case study of concrete pouring using a tower crane. The results of the case study revealed that validation of input parameters, i.e. probability density functions, does not necessarily entail validated simulation models. The calibration method facilitates in-depth analysis of how changes in productivity rates affect simulation forecasts aiming to build reliable models. Once reliable models are available, managers can use simulations to assess how different decisions affect construction outcomes. However, simulation modelling of different options is often complex, laborious, and time-consuming. Therefore, managers rarely use simulation for planning and the possible impacts of different management decisions are unknown. To this end, in Chapter 4, we propose a simulation-driven decision support method to optimise on-site construction process management. Building on the previous chapters, Chapter 4 presents the entire envisioned system for applying simulation as part of the digital twin concept by an integrated method. The simulation-driven method automatically models many options based on inputs for resource allocations, delivery scenarios, and constraint settings. The method applies stochastic discrete event simulation to perform a scenario analyses considering uncertainties when calculating key performance indicators. Additionally, the method performs multi-criteria optimisation to identify Pareto-optimal options. A real-world case study involving the construction of an industrial building validated the method. The results revealed that an option that performs best regarding the mean value can underperform in extreme scenarios, such as the best-case. These insights underscore the need for stochastic investigations when using simulation for decision support. The simulation-driven method simplifies modelling and gives managers a comprehensive understanding of potential outcomes to promote successful construction. This dissertation makes several significant contributions. It provides a systematic approach to integrating real-time data with construction simulations. We developed three advanced analytics methods for automated, continuous, and comprehensive processing of real-time data. The methods enable a data stream to convert collected kinematic data into key performance indicators. By using these methods, managers can gain meaningful insights into construction progress, develop reliable simulation models, and better understand the impacts of decisions before implementation. We validated each method and the entire integrated approach through real-world case studies to provide evidence for the potential of simulation as part of the digital twin concept. The proposed approach aims at iterative management that follows the Plan-Do-Study-Act cycle to support decision-making proactively. Our approach offers significant potential to implement data-driven decision-making and improve performance in the construction industry.","Die Bauindustrie weist Defizite in Bereichen wie Produktivität, Ressourceneffizienz und Kostenkontrollen auf. Diese Defizite sind auf mehrere Faktoren zurückzuführen. Jedes Gebäude ist ein einzigartiges Produkt, das von wechselnden Teams auf zeitlich begrenzten Baustellen unter ständig variierenden Bedingungen errichtet wird. Obwohl die Arbeitsabläufe in Projekten ähnlich sein können, machen die spezifischen Rahmenbedingungen eines jeden Bauprojekts die Planung komplex. Daher ist für jedes Projekt eine angepasste Planung erforderlich, um auf die spezifischen Bedingungen reagieren zu können. Das Baumanagement greift typischerweise auf generische, annahmebasierte oder historische Daten sowie auf wenig anpassungsfähige Planungsmethoden zurück. Diese generischen Ansätze können die inhärenten dynamischen Prozesse von Bauarbeiten nicht widerspiegeln und behindern ein erfolgreiches Management. Mit der fortschreitenden Digitalisierung besteht Potenzial, konventionelle Praktiken zu verändern. Methoden wie die Ereignisorientierte Simulation können helfen, Bauprozesse zu steuern, indem sie Einblicke in zukünftige Entwicklungen geben. Für einen effektiven Einsatz sind jedoch zuverlässige Informationen über den aktuellen Zustand des Systems notwendig. Das Konzept des Digitalen Zwillings zielt darauf ab, kontinuierlich Daten zu sammeln und zu analysieren, um kontextspezifische Informationen zu liefern. Daher kann das Konzept des Digitalen Zwillings die Simulations- und Managementpraktiken in der Bauindustrie erheblich verbessern. Diese Dissertation untersucht, wie der Ansatz des Digitalen Zwillings in die Prozesssimulation integriert werden kann, um das Baumanagement zu optimieren. Wir schlagen ein System vor, um einen Fluss von Echtzeitdaten zu etablieren. Dieses System umfasst drei Advanced Analytics Methoden zur Umwandlung von Rohdaten in quantitative Leistungskennzahlen. Diese Methoden ermöglichen die Analyse von Echtzeitdaten, die Kalibrierung von Simulationsmodellen und die simulationsbasierte Entscheidungsunterstützung. Die Anwendbarkeit jeder Methode wurde in einer realen Fallstudie validiert, um den Nutzen der Methoden zu belegen. Aktivitätsdauern sind ein zentraler Faktor in der Bauplanung. Das Fehlen verlässlicher Werte stellt ein wesentliches Defizit in der Managementpraxis dar. Durch die fortschreitende Digitalisierung können Daten zur Verfolgung des Baufortschritts auf der Baustelle in Echtzeit erfasst werden, z. B. durch intelligente Sensoren. Die Analyse der während der Bauphase gesammelten Daten ermöglicht die Extraktion von datenbasierten Aktivitätsdauern. Bisherige Ansätze zur datenbasierten Extraktion von Aktivitätsdauern konzentrierten sich auf Zykluszeiten oder Durchschnittswerte. Diese übersehen die Variabilität der Dauern und führen somit zu einem Informationsverlust. Zu diesem Zweck schlagen wir in Kapitel 2 eine Methode zur Extraktion individueller Aktivitätsdauern für jede Wiederholung vor. Die Methode nutzt maschinelles Lernen, um kinematische Echtzeitdaten in Aktivitäten zu klassifizieren und automatisch deren Dauern zu extrahieren. Anschließend werden durch eine automatische Nachbearbeitung mit Hilfe logischer Regeln Fehlklassifikationen identifiziert und korrigiert, um die Genauigkeit der Extraktion der Dauer zu verbessern. Wir haben die Anwendbarkeit der Methode in einer Fallstudie für den Arbeitsprozess der Betonage mithilfe eines Turmdrehkrans veranschaulicht, der aus vier aufeinanderfolgenden Aktivitäten besteht. Die Fallstudie zeigte die erfolgreiche Anwendbarkeit der Methode durch den Vergleich der datenbasierten und der gemessenen Dauern. Die Methode widerlegt in der Fallstudie die Annahme einer Normalverteilung für jeden der vier Sätze von Dauern. Die Ergebnisse unterstreichen somit die Notwendigkeit, die Dauern der einzelnen Aktivitätswiederholungen zu extrahieren. Die automatisierte Bereitstellung von projektspezifischen Aktivitätsdauern liefert dem Management aussagekräftige und zuverlässige Informationen über den Baufortschritt. Die Bereitstellung zuverlässiger Aktivitätsdauern ermöglicht genaue Vorhersagen und unterstützt damit den Einsatz von Simulation in der Bauplanung. Durch die Verwendung aktualisierter Dauern können Simulationsmodelle kontinuierlich kalibriert werden. In der Simulationspraxis wird die kontinuierliche Modellkalibrierung jedoch häufig vernachlässigt und die Dynamiken und Unsicherheiten der realen Welt werden in den Prognosen nicht berücksichtigt. Kapitel 3 befasst sich mit diesen Problemen, indem es eine Methode zur automatischen und kontinuierlichen Kalibrierung von Ereignisorientierter Simulation entwickelt. Auf der Grundlage von Zeitreihen verwendet die Methode stochastische Produktivitätsmodellierung, um die am besten geeigneten Wahrscheinlichkeitsdichtefunktionen zur Darstellung von Produktivitätsraten zu ermitteln. Die Methode verwendet die ermittelten Funktionen zur kontinuierlichen Kalibrierung eines Simulationsmodells, um die sich ändernden Bedingungen des Systems widerzuspiegeln. Die Methode validiert die Funktionen und das Simulationsmodell durch statistische Tests. Wir veranschaulichen die Anwendung der Methode anhand einer Fallstudie bezüglich der Betonage mithilfe eines Turmdrehkrans. Die Ergebnisse der Fallstudie zeigen, dass die Validierung der Eingangsparameter, d. h. der Wahrscheinlichkeitsdichtefunktionen, nicht zwingend zu validierten Simulationsmodellen führt. Diese Kalibrierungsmethode ermöglicht eine eingehende Analyse der Auswirkungen von Änderungen der Produktivitätsraten auf die Simulationsprognosen, um zuverlässige Modelle zu erstellen. Sobald zuverlässige Modelle zur Verfügung stehen, können Manager mit Hilfe von Simulationen beurteilen, wie sich verschiedene Entscheidungen auf Bauvorhaben auswirken. Die Simulationsmodellierung verschiedener Optionen ist jedoch oft komplex, ressourcenintensiv und zeitaufwendig. Aus diesem Grund verwenden Manager Simulation nur selten für die Planung, und die möglichen Auswirkungen verschiedener Managemententscheidungen sind unklar. Zu diesem Zweck schlagen wir in Kapitel 4 eine simulationsgesteuerte Methode zur Entscheidungsunterstützung vor, um das Management von Bauprozessen vor Ort zu optimieren. Aufbauend auf den vorangegangenen Kapiteln wird in Kapitel 4 das gesamte geplante System zur Anwendung der Simulation als Teil des Konzepts des Digitalen Zwillings mittels einer integrierten Methode vorgestellt. Die simulationsgesteuerte Methode modelliert automatisch eine Vielzahl an unterschiedlichen Optionen auf der Grundlage von Eingaben für Ressourcenallokationen, Lieferszenarien und Einschränkungen. Die Methode nutzt stochastische Ereignisorientierte Simulation, um Szenarioanalysen unter Berücksichtigung von Unsicherheiten bei der Berechnung von Leistungskennzahlen durchzuführen. Zusätzlich führt die Methode eine multikriterielle Optimierung durch, um Pareto-optimale Optionen zu identifizieren. Die Methode wurde anhand einer realen Fallstudie bezüglich des Baus eines Industriegebäudes validiert. Die Ergebnisse zeigen, dass eine Option, die hinsichtlich des Mittelwertes am besten abschneidet, in extremen Szenarien wie dem Best-Case schlechtere Ergebnisse erzielt. Diese Erkenntnisse unterstreichen die Notwendigkeit stochastischer Untersuchungen bei der Verwendung von Simulationen zur Entscheidungsunterstützung. Die simulationsgestützte Methode vereinfacht die Modellierung und vermittelt Managern ein umfassendes Verständnis der möglichen Ergebnisse, um eine erfolgreiche Baudurchführung zu gewährleisten. Diese Dissertation liefert mehrere wichtige Beiträge. Sie bietet einen systematischen Ansatz für die Integration von Echtzeitdaten in Bausimulationen. Wir haben drei Advanced Analytics Methoden für die automatisierte, kontinuierliche und umfassende Verarbeitung von Echtzeitdaten entwickelt. Die Methoden ermöglichen die Umwandlung von gesammelten kinematischen Daten in Leistungsindikatoren. Mithilfe dieser Methoden können Manager aussagekräftige Einblicke in den Baufortschritt gewinnen, zuverlässige Simulationsmodelle entwickeln und die Auswirkungen von Entscheidungen vor deren Umsetzung besser verstehen. Wir haben jede Methode und den gesamten integrierten Ansatz anhand von Fallstudien aus der Praxis validiert, um das Potenzial der Simulation als Teil des Konzepts des Digitalen Zwillings zu belegen. Der vorgeschlagene Ansatz verfolgt einen iterativen Managementprozess, der dem Plan-Do-Study-Act-Zyklus entspricht, um die Entscheidungsfindung proaktiv zu unterstützen. Unser Ansatz bietet erhebliches Potenzial für die Implementierung datengesteuerter Entscheidungsfindung und die Verbesserung der Performance in der Bauindustrie."]},{"key":"dc:title","label":"Title","values":["Exploring construction process simulation as part of the digital twin concept in real-world contexts"]}]}],"canonical_facts":{"dc:contributor.advisor":["Hartmann, Timo"],"dc:creator":["Jungmann, Manuel"],"dc:date.accessioned":["2025-06-11T08:21:37Z"],"dc:date.available":["2025-06-11T08:21:37Z"],"dc:date.issued":["2025"],"dc:description.abstract":["The construction industry faces problems like low productivity, poor resource efficiency, and cost overruns. Several factors cause these problems. Each building is a unique product, constructed by changing teams on temporary sites under constantly changing conditions. Although operating sequences may be similar across projects, the unique aspects of each construction project make planning complex. Therefore, each project needs adaptable planning to respond to specific conditions. However, construction management usually relies on generic information based on assumptions or historical data and inflexible planning methods. These generic approaches cannot reflect the inherent dynamic processes of construction works and hinder successful management. With advancing digitalisation, there is potential to change conventional practices. Methods like discrete event simulation can help manage construction processes by providing insights regarding future developments. However, reliable information regarding the current state of the system is essential for effective use. The digital twin concept aims to continuously collect and analyse data to provide context-specific information. Therefore, the digital twin concept can significantly improve simulation and management practices in the construction industry. This dissertation explores how the digital twin approach can be integrated with process simulation to optimise construction management. We propose a framework to establish a systematic flow of real-time data. This framework includes three advanced analytics-based methods to convert raw data into quantitative key performance indicators. These methods enable the analysis of real-time data, simulation calibration, and simulation-driven decision support. The applicability of each method was validated in a real-world case study to provide evidence for the methods' usage. Activity durations are the most important factor for construction planning. Thus, the lack of reliable activity durations is a significant shortcoming in management practice. Due to advancing digitalisation, data can be collected to track construction progress on-site in real-time, e.g., by smart sensors. The analysis of data collected during construction enables the extraction of data-based activity durations. Previous approaches for data-based extractions of activity durations focused on cycle times or mean durations. However, cycle times and mean durations are simplifications as valuable information can get lost. To this end, in Chapter 2, we propose a method to extract individual activity durations for each repetition. The method applies machine learning to classify in real time collected kinematic data to recognise activities and extract durations automatically. Subsequently, automatic post-processing using logical rules identifies and corrects misclassifications to improve the accuracy of duration extractions. We illustrated the applicability of the method in a case study for the work process of concreting with a tower crane, which consists of four consecutive activities. The case study demonstrated the successful applicability of the method by comparing the data-based and the measured durations. In the case study, the method disproved normality for each of the four sets of durations. Thus, the results emphasise the need to extract the durations of individual activity repetitions. The automated provision of project-specific activity durations provides management with meaningful and reliable information on construction progress. The provision of reliable activity durations enables accurate forecasts and, thus, supports the implementation of simulation in construction planning. By using updated durations, simulation models can be continuously calibrated. However, simulation practice often misses continuous model calibration and neglects real-world dynamics and uncertainties in forecasts. Chapter 3 addresses these issues by developing a method for automated and continuous discrete event simulation calibrations. Based on sets of durations, the method uses stochastic productivity modelling to identify the most suitable probability density functions representing productivity rates. The method uses the identified functions to continuously calibrate a simulation model to reflect the changing conditions of the system. The method validates functions and the simulation model through statistical tests. We illustrated the application of the method in a case study of concrete pouring using a tower crane. The results of the case study revealed that validation of input parameters, i.e. probability density functions, does not necessarily entail validated simulation models. The calibration method facilitates in-depth analysis of how changes in productivity rates affect simulation forecasts aiming to build reliable models. Once reliable models are available, managers can use simulations to assess how different decisions affect construction outcomes. However, simulation modelling of different options is often complex, laborious, and time-consuming. Therefore, managers rarely use simulation for planning and the possible impacts of different management decisions are unknown. To this end, in Chapter 4, we propose a simulation-driven decision support method to optimise on-site construction process management. Building on the previous chapters, Chapter 4 presents the entire envisioned system for applying simulation as part of the digital twin concept by an integrated method. The simulation-driven method automatically models many options based on inputs for resource allocations, delivery scenarios, and constraint settings. The method applies stochastic discrete event simulation to perform a scenario analyses considering uncertainties when calculating key performance indicators. Additionally, the method performs multi-criteria optimisation to identify Pareto-optimal options. A real-world case study involving the construction of an industrial building validated the method. The results revealed that an option that performs best regarding the mean value can underperform in extreme scenarios, such as the best-case. These insights underscore the need for stochastic investigations when using simulation for decision support. The simulation-driven method simplifies modelling and gives managers a comprehensive understanding of potential outcomes to promote successful construction. This dissertation makes several significant contributions. It provides a systematic approach to integrating real-time data with construction simulations. We developed three advanced analytics methods for automated, continuous, and comprehensive processing of real-time data. The methods enable a data stream to convert collected kinematic data into key performance indicators. By using these methods, managers can gain meaningful insights into construction progress, develop reliable simulation models, and better understand the impacts of decisions before implementation. We validated each method and the entire integrated approach through real-world case studies to provide evidence for the potential of simulation as part of the digital twin concept. The proposed approach aims at iterative management that follows the Plan-Do-Study-Act cycle to support decision-making proactively. Our approach offers significant potential to implement data-driven decision-making and improve performance in the construction industry.","Die Bauindustrie weist Defizite in Bereichen wie Produktivität, Ressourceneffizienz und Kostenkontrollen auf. Diese Defizite sind auf mehrere Faktoren zurückzuführen. Jedes Gebäude ist ein einzigartiges Produkt, das von wechselnden Teams auf zeitlich begrenzten Baustellen unter ständig variierenden Bedingungen errichtet wird. Obwohl die Arbeitsabläufe in Projekten ähnlich sein können, machen die spezifischen Rahmenbedingungen eines jeden Bauprojekts die Planung komplex. Daher ist für jedes Projekt eine angepasste Planung erforderlich, um auf die spezifischen Bedingungen reagieren zu können. Das Baumanagement greift typischerweise auf generische, annahmebasierte oder historische Daten sowie auf wenig anpassungsfähige Planungsmethoden zurück. Diese generischen Ansätze können die inhärenten dynamischen Prozesse von Bauarbeiten nicht widerspiegeln und behindern ein erfolgreiches Management. Mit der fortschreitenden Digitalisierung besteht Potenzial, konventionelle Praktiken zu verändern. Methoden wie die Ereignisorientierte Simulation können helfen, Bauprozesse zu steuern, indem sie Einblicke in zukünftige Entwicklungen geben. Für einen effektiven Einsatz sind jedoch zuverlässige Informationen über den aktuellen Zustand des Systems notwendig. Das Konzept des Digitalen Zwillings zielt darauf ab, kontinuierlich Daten zu sammeln und zu analysieren, um kontextspezifische Informationen zu liefern. Daher kann das Konzept des Digitalen Zwillings die Simulations- und Managementpraktiken in der Bauindustrie erheblich verbessern. Diese Dissertation untersucht, wie der Ansatz des Digitalen Zwillings in die Prozesssimulation integriert werden kann, um das Baumanagement zu optimieren. Wir schlagen ein System vor, um einen Fluss von Echtzeitdaten zu etablieren. Dieses System umfasst drei Advanced Analytics Methoden zur Umwandlung von Rohdaten in quantitative Leistungskennzahlen. Diese Methoden ermöglichen die Analyse von Echtzeitdaten, die Kalibrierung von Simulationsmodellen und die simulationsbasierte Entscheidungsunterstützung. Die Anwendbarkeit jeder Methode wurde in einer realen Fallstudie validiert, um den Nutzen der Methoden zu belegen. Aktivitätsdauern sind ein zentraler Faktor in der Bauplanung. Das Fehlen verlässlicher Werte stellt ein wesentliches Defizit in der Managementpraxis dar. Durch die fortschreitende Digitalisierung können Daten zur Verfolgung des Baufortschritts auf der Baustelle in Echtzeit erfasst werden, z. B. durch intelligente Sensoren. Die Analyse der während der Bauphase gesammelten Daten ermöglicht die Extraktion von datenbasierten Aktivitätsdauern. Bisherige Ansätze zur datenbasierten Extraktion von Aktivitätsdauern konzentrierten sich auf Zykluszeiten oder Durchschnittswerte. Diese übersehen die Variabilität der Dauern und führen somit zu einem Informationsverlust. Zu diesem Zweck schlagen wir in Kapitel 2 eine Methode zur Extraktion individueller Aktivitätsdauern für jede Wiederholung vor. Die Methode nutzt maschinelles Lernen, um kinematische Echtzeitdaten in Aktivitäten zu klassifizieren und automatisch deren Dauern zu extrahieren. Anschließend werden durch eine automatische Nachbearbeitung mit Hilfe logischer Regeln Fehlklassifikationen identifiziert und korrigiert, um die Genauigkeit der Extraktion der Dauer zu verbessern. Wir haben die Anwendbarkeit der Methode in einer Fallstudie für den Arbeitsprozess der Betonage mithilfe eines Turmdrehkrans veranschaulicht, der aus vier aufeinanderfolgenden Aktivitäten besteht. Die Fallstudie zeigte die erfolgreiche Anwendbarkeit der Methode durch den Vergleich der datenbasierten und der gemessenen Dauern. Die Methode widerlegt in der Fallstudie die Annahme einer Normalverteilung für jeden der vier Sätze von Dauern. Die Ergebnisse unterstreichen somit die Notwendigkeit, die Dauern der einzelnen Aktivitätswiederholungen zu extrahieren. Die automatisierte Bereitstellung von projektspezifischen Aktivitätsdauern liefert dem Management aussagekräftige und zuverlässige Informationen über den Baufortschritt. Die Bereitstellung zuverlässiger Aktivitätsdauern ermöglicht genaue Vorhersagen und unterstützt damit den Einsatz von Simulation in der Bauplanung. Durch die Verwendung aktualisierter Dauern können Simulationsmodelle kontinuierlich kalibriert werden. In der Simulationspraxis wird die kontinuierliche Modellkalibrierung jedoch häufig vernachlässigt und die Dynamiken und Unsicherheiten der realen Welt werden in den Prognosen nicht berücksichtigt. Kapitel 3 befasst sich mit diesen Problemen, indem es eine Methode zur automatischen und kontinuierlichen Kalibrierung von Ereignisorientierter Simulation entwickelt. Auf der Grundlage von Zeitreihen verwendet die Methode stochastische Produktivitätsmodellierung, um die am besten geeigneten Wahrscheinlichkeitsdichtefunktionen zur Darstellung von Produktivitätsraten zu ermitteln. Die Methode verwendet die ermittelten Funktionen zur kontinuierlichen Kalibrierung eines Simulationsmodells, um die sich ändernden Bedingungen des Systems widerzuspiegeln. Die Methode validiert die Funktionen und das Simulationsmodell durch statistische Tests. Wir veranschaulichen die Anwendung der Methode anhand einer Fallstudie bezüglich der Betonage mithilfe eines Turmdrehkrans. Die Ergebnisse der Fallstudie zeigen, dass die Validierung der Eingangsparameter, d. h. der Wahrscheinlichkeitsdichtefunktionen, nicht zwingend zu validierten Simulationsmodellen führt. Diese Kalibrierungsmethode ermöglicht eine eingehende Analyse der Auswirkungen von Änderungen der Produktivitätsraten auf die Simulationsprognosen, um zuverlässige Modelle zu erstellen. Sobald zuverlässige Modelle zur Verfügung stehen, können Manager mit Hilfe von Simulationen beurteilen, wie sich verschiedene Entscheidungen auf Bauvorhaben auswirken. Die Simulationsmodellierung verschiedener Optionen ist jedoch oft komplex, ressourcenintensiv und zeitaufwendig. Aus diesem Grund verwenden Manager Simulation nur selten für die Planung, und die möglichen Auswirkungen verschiedener Managemententscheidungen sind unklar. Zu diesem Zweck schlagen wir in Kapitel 4 eine simulationsgesteuerte Methode zur Entscheidungsunterstützung vor, um das Management von Bauprozessen vor Ort zu optimieren. Aufbauend auf den vorangegangenen Kapiteln wird in Kapitel 4 das gesamte geplante System zur Anwendung der Simulation als Teil des Konzepts des Digitalen Zwillings mittels einer integrierten Methode vorgestellt. Die simulationsgesteuerte Methode modelliert automatisch eine Vielzahl an unterschiedlichen Optionen auf der Grundlage von Eingaben für Ressourcenallokationen, Lieferszenarien und Einschränkungen. Die Methode nutzt stochastische Ereignisorientierte Simulation, um Szenarioanalysen unter Berücksichtigung von Unsicherheiten bei der Berechnung von Leistungskennzahlen durchzuführen. Zusätzlich führt die Methode eine multikriterielle Optimierung durch, um Pareto-optimale Optionen zu identifizieren. Die Methode wurde anhand einer realen Fallstudie bezüglich des Baus eines Industriegebäudes validiert. Die Ergebnisse zeigen, dass eine Option, die hinsichtlich des Mittelwertes am besten abschneidet, in extremen Szenarien wie dem Best-Case schlechtere Ergebnisse erzielt. Diese Erkenntnisse unterstreichen die Notwendigkeit stochastischer Untersuchungen bei der Verwendung von Simulationen zur Entscheidungsunterstützung. Die simulationsgestützte Methode vereinfacht die Modellierung und vermittelt Managern ein umfassendes Verständnis der möglichen Ergebnisse, um eine erfolgreiche Baudurchführung zu gewährleisten. Diese Dissertation liefert mehrere wichtige Beiträge. Sie bietet einen systematischen Ansatz für die Integration von Echtzeitdaten in Bausimulationen. Wir haben drei Advanced Analytics Methoden für die automatisierte, kontinuierliche und umfassende Verarbeitung von Echtzeitdaten entwickelt. Die Methoden ermöglichen die Umwandlung von gesammelten kinematischen Daten in Leistungsindikatoren. Mithilfe dieser Methoden können Manager aussagekräftige Einblicke in den Baufortschritt gewinnen, zuverlässige Simulationsmodelle entwickeln und die Auswirkungen von Entscheidungen vor deren Umsetzung besser verstehen. Wir haben jede Methode und den gesamten integrierten Ansatz anhand von Fallstudien aus der Praxis validiert, um das Potenzial der Simulation als Teil des Konzepts des Digitalen Zwillings zu belegen. Der vorgeschlagene Ansatz verfolgt einen iterativen Managementprozess, der dem Plan-Do-Study-Act-Zyklus entspricht, um die Entscheidungsfindung proaktiv zu unterstützen. Unser Ansatz bietet erhebliches Potenzial für die Implementierung datengesteuerter Entscheidungsfindung und die Verbesserung der Performance in der Bauindustrie."],"dc:identifier.uri":["https://depositonce.tu-berlin.de/handle/11303/24944","https://doi.org/10.14279/depositonce-23760"],"dc:language.iso":["en"],"dc:rights.uri":["https://creativecommons.org/licenses/by/4.0/"],"dc:title":["Exploring construction process simulation as part of the digital twin concept in real-world contexts"],"dc:type":["Doctoral Thesis"]},"updated_at":"2026-07-27T21:28:35Z"}