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With the pressure of the increasing density of urban areas, some public infrastructures are moving to the underground to free up space above, such as utility lines, rail lines and roads. In the big data era, the three-dimensional (3D) data can be beneficial to understand the complex urban area. Comparing to spatial data and information of the above ground, we lack the precise and detailed information about underground infrastructures, such as the spatial information of underground infrastructure, the ownership of underground objects and the interdependence of infrastructures in the above and below ground. How can we map reliable 3D underground utility networks and use them in the land administration? First, to explain the importance of this work and find a possible solution, this paper observes the current issues of the existing underground utility database in Singapore. A framework for utility data governance is proposed to manage the work process from the underground utility data capture to data usage. This is the backbone to support the coordination of different roles in the utility data governance and usage. Then, an initial design of the 3D underground utility data model is introduced to describe the 3D geometric and spatial information about underground utility data and connect it to the cadastral parcel for land administration. In the case study, the newly collected data from mobile Ground Penetrating Radar is integrated with the existing utility data for 3D modelling. It is expected to explore the integration of new collected 3D data, the existing 2D data and cadastral information for land administration of underground utilities.

Urbanization of cities demands efficient spatial management. The construction of utility lines significantly alters the spatial landscape. The subsurface space is often neglected, resulting in outdated or absent records of underground utility infrastructure. This clearly underscores the need and importance of maintaining accurate utility records. Modern non-destructive techniques for underground utility detection, such as ground penetrating radar (GPR), can enhance the documentation and mapping of subsurface infrastructure. The subject of this paper is the optimization of GPR survey and processing workflows to improve the accuracy of underground utility detection when using the Leica DS2000. The research comprises both theoretical and experimental analyses, including the application of various GPR data collection methods on test sites. The experimental component of the research was conducted using the Leica DS2000 GPR system. The geospatial data were processed using several software applications, including uNext Advanced, IQMaps, and Geolitix. Based on the multicriteria analysis of these results and an assessment of detection accuracy, an optimal workflow (decision diagram) was defined for the detection of underground utility infrastructure using Leica DS2000 under favorable soil conditions. This study explored the feasibility of efficiently updating the cadastral database of public utility infrastructure through non-invasive technologies, thereby contributing to the improvement of subsurface utility infrastructure management.

The “U-shaped ferrule joint bars connections” have a stable mechanical property, requiring a low level of construction accuracy and a relatively simple connection process, which significantly increase the construction speed. Based on the “U-shaped ferrule joint bars connections” technology, a new type of prefabricated concrete underground utility tunnel was proposed. This prefabricated technology realizes a formwork-free construction and vertical support-free assembly of the top plate on site. Through the full-scale model static test and numerical analyses, the mechanical properties, i.e., the crack development law and bearing capacity, were systematically investigated to validate the effectiveness of the “U-shaped ferrule joint bars connections”. The test results indicated that the performance of the “U-shaped ferrule joint bars connections” is reliable. During the loading process, the prefabricated utility tunnel experienced three stages, i.e., cracking, stiffness degradation, and ultimate failure. The numerical analysis results correlated with the test results well. The simulation results showed that the bearing capacities of the prefabricated underground utility tunnel and the cast-in-place utility tunnel were similar. The longitudinal joint connections of the prefabricated utility tunnel allow the structure as an integration to maintain favourable mechanical properties.

Due to the capabilities of non-destructive testing of inaccessible objects, GPR (Ground Penetrating Radar) is used in geology, archeology, forensics and increasingly also in engineering tasks. The wide range of applications of the GPR method has been provided by the use of advanced technological solutions by equipment manufacturers, including multi-channel units. The acquisition of data along several profiles simultaneously allows time to be saved and quasi-continuous information to be collected about the subsurface situation. One of the most important aspects of data acquisition systems, including GPR, is the appropriate methodology and accuracy of the geoposition. This publication aims to discuss the results of GPR measurements carried out using the multi-channel Leica Stream C GPR (IDS GeoRadar Srl, Pisa, Italy). The significant results of the test measurement were presented the idea of which was to determine the achievable accuracy depending on the georeferencing method using a GNSS (Global Navigation Satellite System) receiver, also supported by time synchronization PPS (Pulse Per Second) and a total station. Methodology optimization was also an important aspect of the discussed issue, i.e., the effect of dynamic changes in motion trajectory on the positioning accuracy of echograms and their vectorization products was also examined. The standard algorithms developed for the dedicated software were used for post-processing of the coordinates and filtration of echograms, while the vectorization was done manually. The obtained results provided the basis for the confrontation of the material collected in urban conditions with the available cartographic data in terms of the possibility of verifying the actual location of underground utilities. The urban character of the area limited the possibility of the movement of Leica Stream C due to the large size of the instrument, however, it created the opportunity for additional analyses, including the accuracy of different location variants around high-rise buildings or the agreement of the amplitude distribution at the intersection of perpendicular profiles.

The traditional construction industry has predominantly relied on labor-intensive methods, resulting in significantly lower efficiency and productivity compared to other industries. According to a report by the Korea Productivity Center (KPC), the productivity of the construction industry is approximately 24.5% lower than that of the manufacturing sector and 15.7% lower than that of the service sector, highlighting a significant productivity gap. To enhance efficiency and productivity in the construction sector, the South Korean government, led by the Ministry of Land, Infrastructure, and Transport, has announced a policy aimed at achieving 100% adoption of smart construction technologies by 2025. In this paper, we propose a methodology for standardizing the format of underground utilities plan drawings by incorporating 3D coordinates, shapes, and attribute information to facilitate the digital transformation of construction site data. Furthermore, we introduce a standardized approach for extracting data from these drawings and converting them into 3D spatial data in the GeoJSON (Geographic JavaScript Object Notation) format. The experimental results of the technology for processing structured drawings into 3D spatial data demonstrated that all data were successfully converted without any omissions.

Terrain conductivity meters (TCMs) are efficient devices for different sorts of subsurface investigations, including detecting and tracing buried utilities, such as metallic pipes and cables. However, data collected using TCMs are usually ambiguous and hard to interpret. This ambiguity originates from the complex shape of apparent conductivity anomalies, the influence of irrelevant conductive bodies, and the interference of random noise with the collected data. To overcome this ambiguity and produce more interpretable apparent conductivity maps, a three-step filtering routine is proposed and tested using different real datasets. The filtering routine begins with applying a Savitzky–Golay (SG) filter to reduce the effect of random noise. This is followed by a modified rolling ball (MRB) filter to convert the complex M-shape of the anomaly into a single trough pointing to the underground utility. Finally, a virtual resolution enhancement (VRE) filter is applied to enhance the pinpointing apex of the trough. The application of the proposed filtering routine to apparent conductivity data collected using different terrain conductivity meters over different utilities in different urban environments shows a significant improvement of the data and an effective ability to reveal masked underground utilities. The proposed triple filtering routine can be a starting point for a new generation of TCMs with a built-in operation mode for instantaneous delineation and characterization of underground utilities in real time.

The construction of traditional underground utility tunnels faces prominent challenges, including high costs, long construction cycles, and limited workspace. Although 3D printing technology offers an effective solution to these issues, its practical application is largely constrained by key performance factors such as the printability, early strength, and interlayer bonding of concrete materials. This study aims to develop a 3D-printable concrete material specifically suited for the construction of underground utility tunnels. Through collaborative optimization of parameters such as the water–binder ratio, additives, and fiber content using single-factor and orthogonal tests, the optimal mix proportion was determined: a water–binder ratio of 0.30, a 10% dosage of rapid-hardening sulphoaluminate cement (R·SAC), a sand-to-binder ratio of 1.0, 20% mineral admixtures (15% fly ash + 5% silica fume), and a 1.0% volume fraction of polypropylene fibers. The results indicate that the fresh paste achieved a flowability of 192 mm, demonstrating excellent printability. Specimens printed using a sawtooth toolpath reached a 3-day compressive strength of 37.8 MPa, with 28-day compressive and flexural strengths increasing to 56.3 MPa and 7.8 MPa, respectively, and an interlayer bond strength of 3.5 MPa. Crucially, the compressive and flexural anisotropy coefficients were as low as 0.023 and 0.066, respectively, showing a preliminary exploratory trend superior to levels reported in some literature and suggesting the potential of printed components to improve structural performance consistency. This material system not only meets the requirements of 3D printing for early strength and workability but also, by introducing R·SAC to form a low-alkalinity binder system, provides a potential pathway for enhancing long-term durability in corrosive environments. This study offers a reliable theoretical and experimental basis for the application of 3D printing technology in underground engineering. Long-term durability will remain a primary focus of subsequent research.

In hot and humid regions, urban underground utility tunnels are susceptible to high temperature and humidity due to moist inlet air, cable heat dissipation, and limited ventilation jointly affecting the internal environment. To address this issue, an alternating ventilation strategy, in which fan operation is periodically reversed to switch between air supply and exhaust, is proposed. Compared to conventional mechanical ventilation, this strategy overcomes the constraints of unidirectional airflow and mitigates thermal and humidity stratification, with low retrofit requirements and good adaptability. Ventilation performance was evaluated using non-guarantee rates for temperature and relative humidity, i.e., the ratio of the number of measurement points where the temperature/relative humidity exceeds 40 °C/65% to the total number of measurement points in the utility tunnel (TNGR and RHNGR), non-uniformity coefficients (KT and KRH), and mean temperature (Tm). The alternating mode outperformed the conventional mode, reducing TNGR by 6.0% and Tm by 0.3 °C while improving temperature and humidity distributions and lowering cable temperatures. Although the reduction in Tm appears modest, it is practically meaningful because it helps weaken thermal stratification and local overheating, improves cable operating conditions, and may reduce the need for high-airflow operation when tunnel temperatures approach the permissible limit. Response surface methodology was further used to optimize the alternating ventilation parameters, indicating that the recommended fan commutation frequency is 2 under different inlet air temperatures. CFD validation confirmed the effectiveness of the optimized scheme. At an inlet air temperature of 35 °C, KRH decreased from 11.9% to 11.0% and Tm decreased from 37.5 °C to 36.9 °C.

The defect detection in the urban underground utility tunnel faces the challenges of low illuminance and large shadow region. The images collected have problems such as noise and uneven illumination, posing higher requirements for the image feature extraction ability of the network model. A residual feature pyramid attention network based on dense connections (RFPADNet) is proposed in the defect detection of the urban underground comprehensive pipe in this paper. The proposed network consists of three dense blocks, and each dense block uses four residual feature pyramid attention models (RFPAM) as the main feature extractors. Its focus is to utilize residual learning to obtain the fused multi-scale feature information. Soft mask branches are added to the multi-scale channels to enhance the network model’s ability to extract positive features. The experimental results show that the network model proposed in this paper has a training accuracy of 97.50%. Compared with the existing network models, it could achieve better results in various test sets, which has good feasibility and practicality.

Level of detail (LoD) is a key concept for 3D city modeling to optimise visualisation. The LoDs of CityGML shows this trend. This paper explores the relevance of having LoD for visualising 3D model of Underground Utility Networks (UUN). A new approach is proposed for designing multiple LoDs modeling in creating an explicit link between the content of the 3D model and the decision-making process (or operational tasks) to be performed by a user. This Multiple Level of detail Approach (MLA) is divided into four steps. The first step requires defining the visualisation needs in terms of five variables (geometry, topology, semantic, contextual information, and semiology). Next, tasks to be performed are analyzed and categorized. Finally, a matrix of possible LoDs is created for all tasks and the minimum LoD required is proposed. In this paper, we applied this approach for the use case of granting connection permits to water and sewer networks. Learning aspects are proposed in the discussion.