An interactive digitalisation platform for modelling TBM tunnel construction and long-term performance

Real-time perception of rock–machine interaction information in TBM tunnelling using muck image analysis

Unexpected geologic heterogeneities in tunnel lines often cause safety issues and economic losses during excavation using tunnel boring machines (TBMs). Therefore, collecting geologic information ahead of the excavation front is important to prevent geologic disasters and ensure excavation safety. To predict the geologic conditions ahead of the TBM-excavated tunnel in the water supply project at the Songhua River in China, the tunnel-seismic-while-drilling (TSWD) method was adjusted and then applied. First, common-source gather data were acquired to record TBM drilling noise (pilot signal and seismic waves); a processing approach (including normalization, crosscorrelation, wavefield separation, and migration) was used to image the major relevant geologic heterogeneities ahead of the excavation front. A synthetic study from a numerical model with a fault verified the feasibility of the modified TSWD method to identify and image geologic interfaces. Then, this method was applied to field data resulting from a TBM tunnel excavation project. The field test of the modified TSWD in the TBM tunnel revealed fractured zones, fault boundaries, and a preexcavated tunnel ahead, providing the required geologic information for TBM tunneling and preventing geologically-caused safety issues.

The outfall tunnel, which forms part of the Musaimer Pump Station and Outfall project (MPSO), is a 10.2 km long subsea tunnel that extends from the south coast of Doha, starting onshore, and continues under the Arabian Gulf to end in a connection with a diffuser field via riser shaft. The excavation of the outfall tunnel using a Tunnel Boring Machine (TBM) with a 4.42 m diameter and 180 m long backup, starts from a 40 m deep shaft and has an inclination of 0.05% upward to the riser shaft. The tunnel is constructed by precast concrete elements of 3.7 m internal diameter passing through different geological formations typical in the State of Qatar. Although planning a TBM tunnel involves a significant amount of engineering knowledge and experience, what is equally challenging is the enabling works which are required to be designed and constructed before the TBM even arrives at the site. On this project, these works which were substantial within themselves commenced 16 months before the TBM arrived on site. This paper will discuss the planning and execution of these outstanding underground enabling works, the logistic shaft required to facilitate the assembly and launch of the TBM at the initial stage of tunnelling and also how to arrange the sequence and space in terms of TBM logistics supply requirements to allow simultaneous construction of key permanent structures, namely the drop shaft structure which resulted in 6 months saving on the overall completion on this section of the works.

Identification of soil condition at the working face of a tunnel boring machine (TBM) is a key factor for the efficiency and safety of TBM tunneling. The paper presents the first application of the Horizontal-to-Vertical Spectral Ratio (HVSR) method on microtremors induced by a TBM during tunnelling. The innovative application is based on the development of an easy-to-use and economical early-warning system, which aims to confirm, or otherwise, the soil profile established in the design phase of tunnels by comparing the soil natural frequencies obtained from the soil profile carried out during the design phase and the soil natural frequencies coming from the HVSR analysis of the microtremors induced by the TBM during tunnelling. Just one or two geophones are necessary to use the proposed procedure. It can be applied to an area up to about 20 m ahead of the TBM excavation front and constitutes a powerful early warning system. Due to the great heterogeneity of the subsoil, dual-mode TBMs are often used, frequently changing from Open-Face (OF) mode for rock formations to Earth Pressure Balance (EPB) mode for cohesive and incoherent soil. Any “additional” information on the soil, which will be dug in the next days or hours is extremely useful for subsoil with great heterogeneity. The new procedure offers a reasonable time interval in which to modify the excavation method. This, in turn, can avoid damage to the TBM and existing structures and infrastructures. It allows us also to have a valuable geotechnical database for future works on the infrastructural networks. The proposed procedure has been successfully applied during the construction of the new underground lines in Catania (Italy).

Rapid population growth and the continuous restructuring of economic relationships have significantly increased global demand for efficient transportation systems. In this context, accurate prediction of the Rate of Penetration (ROP) of the Tunnel Boring Machine (TBM) is crucial for construction planning, cost estimation, and real-time operational decision-making in TBM tunneling. When TBMs are appropriately selected in terms of type and capacity according to route conditions and are operated in compliance with sound engineering principles, they enable the excavation of tunnels at very high rate of penetration while maintaining economic feasibility. Estimating tunnel completion time based on geological and geotechnical conditions along the tunnel alignment and the operational capacity of the TBM has been one of the most intensively studied topics in tunneling research over the past two decades. However, recent advances in artificial intelligence (AI) techniques offer significant potential for achieving higher predictive performance in ROP estimation. In light of these developments, this study evaluates the performance of various AI algorithms using data obtained from the T2 tunnel of the Bahçe–Nurdağ (Türkiye) twin tunnels, the longest railway tunnels in Türkiye. In addition, synthetic input parameters were generated to enhance prediction accuracy beyond that achieved in previous studies. The results demonstrate that incorporating these synthetic input parameters leads to improved model performance, with an increase of up to 2.65% in terms of the correlation coefficient. Given the already high predictive capability achieved without synthetic inputs (R² = 0.8637), the improvement obtained in this study (R² = 0.8866) is particularly noteworthy. Overall, the findings indicate that ensemble-based artificial intelligence models incorporating synthetic input data can predict ROP of TBM with very high accuracy, thereby offering a robust and reliable tool for estimating tunnel completion times in TBM tunneling projects.

This study aims to explore the role of traditional architectural elements in promoting sustainability and how to integrate them into modern buildings by utilizing modern technologies, especially Building Information Modeling (BIM), to restore heritage identity and apply green building standards to modern residential buildings. Despite the advancements in modern urban and architectural developments, sustainability is an ancient concept. Traditional architecture in our region has successfully balanced the use of natural resources with environmental preservation, thereby providing cultural and environmental identity to heritage buildings. In Aleppo, modern residential buildings suffer from a loss of this deep connection to the environment and authentic identity. This research employs BIM software to model and analyze the heritage house of Ajqabash, assessing its compliance with green building standards. The study highlights the role of modern BIM technologies in facilitating environmental analysis and emphasizes the importance of traditional elements, such as the iwan and the internal courtyard, in providing thermal comfort for residents. By studying and analyzing traditional architectural elements and using BIM techniques, the article demonstrates that the concept of sustainability is deeply rooted in our architectural heritage. The Revit program was utilized to model the Ajqabash house in its architectural and structural details, and an energy simulation plug-in was used to analyze the sun's impact on the house. The findings underscore the effectiveness of BIM in conducting environmental studies and in preserving heritage while promoting sustainability. The goal is to develop new design criteria that combine heritage architectural identity with global sustainability standards. This approach aims to contribute to building environmentally and economically sustainable architectural communities and providing innovative solutions to modern housing problems in Aleppo. Ultimately, the research seeks to honor our rich architectural heritage while addressing contemporary environmental challenges.

The Fusion of GIS and Building Information Modeling for Big Data Analytics in Managing Development Sites

Tunnelling with a Tunnel Boring Machine (TBM) offers several advantages over conventional tunnelling methods when covering long distances, particularly regarding time, safety, and environmental impact. Prior to starting a TBM project, performance analysis is a crucial stage necessary to inform the initial investment decision and project cost estimation. However, this analysis requires extensive fieldwork and laboratory studies. The Advance Rate (AR), calculated from the Utilization Factor (U) – the ratio of excavation time to total project time – is a widely accepted measure of TBM performance. Total project time encompasses various parameters, including boring time, cutter inspection and replacement times, and constant factors like re-gripping, service, and maintenance times. In this study, we employed analytical methods, statistical analysis, and genetic programming approaches to evaluate the AR and thus enhance TBM performance analysis. Gamma tests were conducted to identify interrelated parameters in TBM operations. The results from these tests showed that the Applied Force per Cutter (FN) and penetration values (together forming the Field Penetration Index or FPI) are useful in estimating cutter consumption. While FN can be determined through laboratory tests, its field determination is challenging. We have developed several charts and a function derived from genetic programming to estimate the AR of a TBM. The study demonstrated that this function, formulated using genetic programming, possesses strong predictive capability and can significantly improve TBM performance analysis.

The building and construction industry has witnessed many developments during the past years. The most important of which is the use of building information modeling (BIM). BIM technologies contributed to the development of this industry significantly, as it was used at the level of individual buildings and projects. Nowadays the continuous need for development forced the transition to use BIM on a wider level to include cities, through integration with other systems, mainly Geographical Information Systems (GIS). The integration between BIM and GIS will contribute to obtaining digital models for cities or as called City Information Modeling (CIM). CIM will contribute significantly to supporting the decision-making process in addition to developing urban planning processes for cities. Furthermore, these CIM models will form the base to contribute in creating digital twins in order to conduct smart city. In this research paper, we will elaborate an approach about how to take advantage of the overlap and integration between BIM and GIS in order to create a CIM model without using IFC and City GML formats for a small region in Nizhny Novgorod in Russian Federation. As a result of this research, we will get a CIM model without losing features of 3D BIM models. KeywordsDigital modelsBuilding information modelingGeographical information systemsCity information modelingDatabase integration

Tunnel Boring Machines (TBMs) play a vital role in modern tunnel construction due to their efficiency and adaptability across various geological conditions. However, when tunneling through geologically altered zones, such as fault zones and soft ground, significant challenges arise, including increased wear, reduced advance rates, and heightened safety risks. This study explores advanced support measures for TBM excavation in altered zones, focusing on deformation characteristics and the bearing capacity of different support strategies in both normal and altered rock masses. Through numerical simulations on the FLAC3D platform, we compare the stress distribution and disturbance evolution in the surrounding rock with and without pre-reinforcement, as well as with single-layer and double-layer pipe-shed supports. The findings reveal that a lack of advance support leads to severe surrounding rock instability and an increased risk of TBM jamming. While single-layer pipe roofing offers partial mitigation, its limited stiffness constrains its effectiveness. In contrast, double-layer large pipe roofing provides superior bending stiffness, effectively minimizing settlement and deformation while significantly reducing the likelihood of TBM jamming. These findings offer practical guidance for the design and implementation of robust TBM support systems, enhancing tunneling safety and efficiency in complex geological conditions.

Information mined from building information models as well as associated geographical data and Geographic Information System (GIS) analyses can increase the success of construction processes and asset management, including buildings, roads, and public facilities. The integration of information from both domains requires high expertise in both spheres. The existing B.Sc and M.Sc. programs linked to the built environment at the Technical University of Munich offer courses for the Building Information Model (BIM) and GIS that are distributed among study programs in Civil Engineering, Architecture, and Geomatics. Students graduating as professionals in one of these domains rarely know how to solve pre-defined technical problems associated with the integration of information from BIM and GIS. Students in such programs seldom practice skills needed for the integration of information from BIM and GIS at a level that is needed in working life. Conversely, the technologies in both domains create artificial boundaries that do not exist in reality—for example, water and electricity would not be of use if the utilities terminated in front of buildings. To bring a change and bridge the gap between BIM and GIS, a change in the teaching methods of BIM/GIS needs to be considered. The Technical University of Munich (TUM) has developed a master’s course (M.Sc. course) for students in Geoinformatics which focuses on competencies required to achieve BIM/GIS integration. This paper describes the course development process and provides a unique perspective on the curriculum and subjects. It also presents the course objective, course development, the selection and development of learning materials, and the assessment of the intended learning outcome of the course. The developed course is validated through a questionnaire, and feedback is provided by participants of the BIM/GIS integration workshop representing a panel of experts in the domain.

Abstract. In recent years, 3D virtual indoor/outdoor urban modelling becomes a key spatial information framework for many civil and engineering applications such as evacuation planning, emergency and facility management. For accomplishing such sophisticate decision tasks, there is a large demands for building multi-scale and multi-sourced 3D urban models. Currently, Building Information Model (BIM) and Geographical Information Systems (GIS) are broadly used as the modelling sources. However, data sharing and exchanging information between two modelling domains is still a huge challenge; while the syntactic or semantic approaches do not fully provide exchanging of rich semantic and geometric information of BIM into GIS or vice-versa. This paper proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graphs. The novelty of the proposed solution comes from the benefits of integrating BIM and GIS technologies into one unified model, so-called Integrated Geospatial Information Model (IGIM). The proposed approach consists of three main modules: BIM-RDF and GIS-RDF graphs construction, integrating of two RDF graphs, and query of information through IGIM-RDF graph using SPARQL. The IGIM generates queries from both the BIM and GIS RDF graphs resulting a semantically integrated model with entities representing both BIM classes and GIS feature objects with respect to the target-client application. The linkage between BIM-RDF and GIS-RDF is achieved through SPARQL endpoints and defined by a query using set of datasets and entity classes with complementary properties, relationships and geometries. To validate the proposed approach and its performance, a case study was also tested using IGIM system design.

In the domain of building management, the absence of timely damage assessment and proactive preventive intervention plans, compounded by inherent aging processes, accelerates the degradation of structures. Current systems for condition assessment and decision support exhibit deficiencies in capturing and retrieving detailed information, resulting in ineffective decisions and significant financial losses. This comprehensive literature review explores condition assessment and maintenance of existing buildings through the integration of geographic information system (GIS) and building information modelling (BIM). GIS and BIM are essential tools for digitising data on the functional, spatial, and physical aspects of buildings. The review emphasises the synergistic potential of GIS and BIM, addressing challenges in interoperability, data modelling, and visualisation. Key findings underscore the importance of improved architectural frameworks to facilitate seamless data integration across diverse GIS, BIM, and facility management tools. The paper also highlights future research directions, including the integration of deep learning and machine learning for predictive maintenance of buildings integrated to their surroundings. In addition, it emphasises the role of Internet of Things technologies, automation, and advancements in 3D GIS and virtual reality for enhancing data collection, visualisation, and decision-making aspects in building condition assessment and maintenance.

This systematic review and meta-analysis investigates the evolving role of Building Information Modeling (BIM) in managing multidimensional risks associated with sustainable bridge infrastructure projects. Drawing upon the PRISMA methodology, the study rigorously screened and analyzed a total of 87 peer-reviewed journal articles and conference papers published between 2010 and 2024. The selected literature was sourced from five leading academic databases—Scopus, Web of Science, IEEE Xplore, ScienceDirect, and the ASCE Library—ensuring comprehensive coverage of global research on BIM-based risk management practices. The review focuses on how BIM contributes to the identification, analysis, mitigation, and continuous monitoring of technical, financial, environmental, and operational risks throughout the lifecycle of bridge projects. Findings reveal that BIM’s capabilities extend well beyond 3D visualization, offering robust tools for 4D scheduling, 5D cost estimation, and real-time data integration that collectively enhance proactive decision-making. BIM facilitates early risk detection through clash detection, geospatial analysis, and scenario simulations during the planning and design stages, reducing design inconsistencies and constructability issues. Moreover, the study synthesizes evidence demonstrating how BIM supports cost and schedule risk control by enabling dynamic simulation of construction sequences and integration with live pricing data, resulting in reduced cost overruns and schedule delays. The review also highlights the increasing convergence of BIM with Internet of Things (IoT) technologies, digital twins, and Geographic Information Systems (GIS), enabling real-time monitoring of structural health, predictive maintenance, and environmental risk modeling in operational phases. Lifecycle-oriented risk governance through BIM emerged as a key theme, particularly in studies focused on sustainability assessments, material optimization, and long-term asset performance. Institutional adoption patterns, regulatory influences, and policy frameworks were also examined, revealing that countries with mandated BIM protocols exhibit more advanced integration of BIM into public infrastructure risk management. Despite persistent challenges such as fragmented data standards, skill gaps, and limited interoperability, the majority of the reviewed literature—accumulating over 3,400 citations—converges on the conclusion that BIM is an essential enabler of risk-informed, sustainable bridge engineering.

Tunnel Boring Machines (TBMs) play a pivotal role in modern underground construction, especially for tunneling through complex geological environments. Accurate real-time classification of surrounding rock masses is critical for optimizing TBM operation, yet conventional methods relying on engineering labels often suffer from subjectivity and label noise. To address these challenges, we propose a novel semi-supervised teacher-student framework that integrates engineering expertise, class prototype learning, and a Graph Neural Network (GNN)-based self-distillation mechanism. The model leverages soft labels, generated from domain-specific knowledge, to incorporate label uncertainty while improving feature cohesion. By employing self-supervised learning and iterative refinement of class prototypes, the framework effectively minimizes label noise, enhances classification boundaries, and adapts to unseen data. Experimental results on a real-world TBM tunneling dataset demonstrate that the proposed method significantly improves classification performance, offering more reliable and robust predictions in the presence of noisy labels. This work lays the foundation for future advancements in AI-driven geological inference, providing enhanced safety, efficiency, and reliability for TBM tunneling projects.