Subsurface Utility Research Articles

Towards the Development of Malaysia’s Subsurface Asset Management Framework

Underground utility detection survey has been practiced in Malaysia for a long time but it was never regulated. While there are guidelines to govern the practice of utility detection survey, an enabling clause is required to regulate the practice. The aim of this paper is to study the available subsurface asset management framework and to propose a framework for subsurface asset management in Malaysia. The methodology to carry out this research shall involve data collections from stakeholders in the form of interviews, questionnaires and discussions. A case study of an underground utility detection survey shall provide data on the process flow a project which involves stakeholders and the authorities. The final input shall be in the form of intellectual discourses with expert committees related to subsurface utility survey and policy makers. From the findings, it was observed that there were significant discrepancies between the engineering and cadastral survey records. While the engineering survey information related to subsurface utility was up to date, the cadastral survey record was never updated. Furthermore, it was observed that the is no enabling clause in existence to govern the management of subsurface assets. The result of this research is a proposed framework for subsurface asset management based on the data collected. In conclusion, it was recommended for the subsurface asset management framework to be tested in the real working environment and to continue with the efforts to realise the framework.

Experimental investigations to evaluate surfactant role on absorption capacity of nanofluid for CO2 utilization in sustainable crude mobilization

Carbon dioxide (CO2) utilization for oilfield applications is affected by viscous fingering which leads to premature breakthrough without showing any appreciable impact on oil recovery and storage. Therefore, to control these issues, CO2 is often injected with high viscous fluid i.e. nanofluid. However, nanofluid efficacy can be further improved by inclusion of a surfactant that not only increases specific area of CO2 [via interfacial tension (IFT) reduction] but also increases CO2 absorption capacity of nanofluid through formation of foam. Thus, in this study, a new colloidal solution of anionic surfactant (i.e. sodium dodecyl sulfate, SDS, 0.16–0.24 wt%) and polymer based single-step silica nanofluid of varying concentration (0.1–1.0 wt%) was proposed for subsurface carbon utilization in CO2 absorption, IFT reduction, and fossil fuel displacement. CO2 absorption experiments showed that absorption was positively influenced by pressure and NP concentration, while increasing temperature showed reverse impact on CO2 absorption. In addition, SDS ensured formation of a Pickering foam which made CO2 to retain inside for over 10 days (an increase of 67%) in SDS treated nanofluid, higher than 6 days of simple nanofluid. Pickering CO2 foam of SDS treated nanofluids was also envisaged for IFT reduction of crude oil, to identify role of these nanofluids in crude mobilization from porous reservoir. CO2 absorption capacity of nanofluid exhibited inverse relationship with IFT value of crude oil. Finally, displacement experiments were conducted which also support inclusion of SDS in silica nanofluid for reduced water cut and higher fossil fuel recovery from porous media. Initially, the oil recovery was only 42% with water and 52% with 1 wt% NP concentration. However, the highest oil recovery was 61% when 1 wt% NP/0.16 wt% SDS combination was used.

Cognitive GPR for Subsurface Object Detection Based on Deep Reinforcement Learning

Ground penetrating radars (GPRs) carried by mobile platforms, such as vehicles and drones, have been applied in various applications, for instance, subsurface utility detection, structural health inspection, and autonomous driving. However, existing GPR systems are not able to operate autonomously and adaptively due to several challenges, including the lack of intelligence, uncertain and dynamic nature of sensing environments, and huge state and action spaces. To overcome these challenges, in this article, we propose an autonomous cognitive GPR (AC-GPR) enabled by a deep reinforcement learning (DRL) approach. Specifically, the operation of the proposed AC-GPR is first formulated as a sequential decision process. A novel reward function is developed for the DRL model by defining and combining two different types of entropy-based rewards resulting from object detection and recognition, respectively. A deep Q-learning network (DQN) is developed to address the extreme curse of dimensionality in the state space and learn a policy directing the actions of the AC-GPR. The AC-GPR is evaluated using software called GprMax by combining DRL with GPR modeling and simulation. Results show that our proposed DRL-based AC-GPR outperforms other GPR systems using different approaches in terms of detection accuracy and operating time.

From underground utility survey to land administration: An underground utility 3D data model

Subsurface utility infrastructure is omnipresent in the often highly contested shallow layers of the subsurface of densely urbanised areas. Reliable information on the location of such utilities is pivotal for the planning and management of such space. Land administration tasks such as ownership management, land acquisition, planning and (re)development depend on and benefit strongly from the availability of reliable information of sufficient quality on the underground including utilities. The objective of the Digital Underground project in Singapore is to establish an authoritative dataset of underground utilities that aims to serve the needs of land administration practices. The work described in this paper aims to develop an underground utility 3D data model to manage and integrate various data from underground utility survey to underground utility data using in land administration. The Land Administration Domain Model (LADM) provides a formal language to manage the rights, responsibilities and restrictions information of land (or water), and the geospatial components. The Underground Utility Data Model (UUDM) inherits some definition from LADM to manage the information related to underground utility survey and land administration. This work provides a preliminary to explore the modelling and using underground utility information in land administration. It can help to develop the data model as a standard for underground utility data management and quality control in the next step.

Modeling dynamic performance of urban infiltration trench systems: Methodology and a case study in Philadelphia

Infiltration trenches are popular green stormwater infrastructure (GSI) systems in dense urban areas primarily due to their small footprints that cope with the limited available space and subsurface utility conditions. To investigate the dynamic, seasonal performance of urban infiltration trench systems, more than three years of continuous rainfall and water level data from an infiltration trench in Philadelphia, Pennsylvania, were analyzed. The recession rates in the infiltration trench showed a seasonality (higher recession rates in warmer months and lower recession rates in colder months) and a head-dependency (higher recession rates in higher water levels). To incorporate this dynamic performance into a simple modeling approach that can be used in practical applications, both event-based and continuous simulation models were developed using storage units in SWMM, and calibrated/validated against observed data. The most important conclusions for dynamic modeling of infiltration trench systems were the introduction of monthly adjustment factors for saturated hydraulic conductivity (Ksat) and a head-dependent component for infiltration to the surrounding in-situ soil in the model, both of which were determined by calibration. Benefits of the presented dynamic modeling approach were demonstrated by comparing the dynamic and static modeling results, indicating the capability of the dynamic approach to promote sustainable and resilient design of infiltration trench systems.

OGC CityGML 3D City Models Enriched with Utility Infrastructures for Developing Countries

Momentous studies have substantiated semantically enriched 3D city models with numerous urban related applications. Utility networks plays a vital role, hence detailed 3D data modeling of utility networks along with 3D urban space can perform various simulations and analysis on urban planning, utility and asset management. Detailed representation of utility networks is not yet studied till now, as they are least considered in city modeling. Subsurface utility infrastructures are connected to several network components that can be installed ‘on-surface’, ‘above-surface’ or ‘below-surface’. CityGML is excessively used for 3D city modeling, hence CityGML compliant utility network modeling is proposed. The study proposes OGC CityGML-based detailed 3D modeling approach of ‘subsurface and overhead’ networks along with several ‘overhead, on-surface and subsurface’ network components. Data modeling approach is implemented with a modified version of CityGML Utility Network Application Domain Extension (ADE) to hold geometric and semantic aspects of subsurface and overhead infrastructures. Also, various Levels of Details (LOD) have proposed in the study. Related relevant applications of utility network modeling are also discussed. Several use cases such as (1) network analysis and (2) affects of utility network excavation on street space; are implemented with CityGML Utility Network ADE data. This study can be considered as a unique study dealing with detailed modeling of utility networks at different levels of details.

Mapping Subsurface Utility Pipes by 3-D Convolutional Neural Network and Kirchhoff Migration Using GPR Images

In this article, we focus on ground-penetrating radar (GPR) for subsurface utility pipe detection. Due to the dense and high-speed 3-D monitoring, GPR is a promising tool. However, because of enormous amount of radar data and difficulty of interpretation, inspection time and cost are the bottlenecks. In this article, we propose a novel detection algorithm by the combination of 3-D convolutional neural network (3-D-CNN) and Kirchhoff migration. A 3-D-CNN architecture was trained utilizing transverse and longitudinal pipes’ measurement data. The classification accuracy of the developed model was about 91%, accurately estimating the pipes’ existences and directions. The 3-D-CNN improved the classification accuracy by about 6% compared to 2-D-CNN in the case of transverse pipes by considering the 3-D geometries of the pipes. After box-by-box search by 3-D-CNN, Kirchhoff migration was applied to cross section images and peaks were extracted. From the result of experimental field data, the algorithm provides the clear understandings of pipes’ 3-D positions and arrangement with reasonable calculation time.

High-resolution analysis of the physicochemical characteristics of sandstone media at the lithofacies scale

Abstract. The prediction of physicochemical rock properties in subsurface models regularly suffers from uncertainty observed at the submeter scale. Although at this scale – which is commonly termed the lithofacies scale – the physicochemical variability plays a critical role for various types of subsurface utilization, its dependence on syndepositional and postdepositional processes is still subject to investigation. The impact of syndepositional and postdepositional geological processes, including depositional dynamics, diagenetic compaction and chemical mass transfer, onto the spatial distribution of physicochemical properties in siliciclastic media at the lithofacies scale is investigated in this study. We propose a new workflow using two cubic rock samples where eight representative geochemical, thermophysical, elastic and hydraulic properties are measured on the cubes' faces and on samples taken from the inside. The scalar fields of the properties are then constructed by means of spatial interpolation. The rock cubes represent the structurally most homogeneous and most heterogeneous lithofacies types observed in a Permian lacustrine-deltaic formation that deposited in an intermontane basin. The spatiotemporal controlling factors are identified by exploratory data analysis and geostatistical modeling in combination with thin section and environmental scanning electron microscopy analyses. Sedimentary structures are well preserved in the spatial patterns of the negatively correlated permeability and mass fraction of Fe2O3. The Fe-rich mud fraction, which builds large amounts of the intergranular rock matrix and of the pseudomatrix, has a degrading effect on the hydraulic properties. This relationship is underlined by a zonal anisotropy that is connected to the observed stratification. Feldspar alteration produced secondary pore space that is filled with authigenic products, including illite, kaolinite and opaque phases. The local enrichment of clay minerals implies a nonpervasive alteration process that is expressed by the network-like spatial patterns of the positively correlated mass fractions of Al2O3 and K2O. Those patterns are spatially decoupled from primary sedimentary structures. The elastic properties, namely P-wave and S-wave velocity, indicate a weak anisotropy that is not strictly perpendicularly oriented to the sedimentary structures. The multifarious patterns observed in this study emphasize the importance of high-resolution sampling in order to properly model the variability present in a lithofacies-scale system. Following this, the physicochemical variability observed at the lithofacies scale might nearly cover the global variability in a formation. Hence, if the local variability is not considered in full-field projects – where the sampling density is usually low – statistical correlations and, thus, conclusions about causal relationships among physicochemical properties might be feigned inadvertently.

Rapid multi-scale analysis of near-surface geophysical anomaly maps: Application to an archaeo-geophysical data set

In near-surface geophysics, ground-based mapping surveys are routinely employed in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. The resulting geophysical anomaly maps of, for example, magnetic or electrical parameters are usually interpreted to laterally delineate subsurface structures such as those related to the remains of past human activities, subsurface utilities and other installations, hydrological properties, or different soil types. To ease the interpretation of such data sets, we propose a multi-scale processing, analysis, and visualization strategy. Our approach relies on a discrete redundant wavelet transform (RWT) implemented using cubic-spline filters and the à trous algorithm, which allows to efficiently compute a multi-scale decomposition of 2D data using a series of 1D convolutions. The basic idea of the approach is presented using a synthetic test image, while our archaeo-geophysical case study from North-East Germany demonstrates its potential to analyze and process rather typical geophysical anomaly maps including magnetic and topographic data. Our vertical-gradient magnetic data show amplitude variations over several orders of magnitude, complex anomaly patterns at various spatial scales, and typical noise patterns, while our topographic data show a distinct hill structure superimposed by a microtopographic stripe pattern and random noise. Our results demonstrate that the RWT approach is capable to successfully separate these components and that selected wavelet planes can be scaled and combined so that the reconstructed images allow for a detailed, multi-scale structural interpretation also using integrated visualizations of magnetic and topographic data. Because our analysis approach is straightforward to implement without laborious parameter testing and tuning, computationally efficient, and easily adaptable to other geophysical data sets, we believe that it can help to rapidly analyze and interpret different geophysical mapping data collected to address a variety of near-surface applications from engineering practice and research.

Beneath The Surface

Investigations related to subsurface utility engineering (SUE) traditionally have been used on linear infrastructure projects to provide detailed utility information within the public right-of-way....

Field Study of Soil Vapor Extraction for Reducing Off-Site Vapor Intrusion.

Soil vapor extraction (SVE) is effective for removing volatile organic compound (VOC) mass from the vadose zone and reducing the potential for vapor intrusion (VI) into overlying and surrounding buildings. However, the relationship between residual mass in the subsurface and VI is complex. Through a series of alternating extraction (SVE on) and rebound (SVE off) periods, this field study explored the relationship and aspects of SVE applicable to VI mitigation in a commercial/light-industrial setting. The primary objective was to determine if SVE could provide VI mitigation over a wide area encompassing multiple buildings, city streets, and subsurface utilities and eliminate the need for individual subslab depressurization systems. We determined that SVE effectively mitigates offsite VI by intercepting or diluting contaminant vapors that would otherwise enter buildings through foundation slabs. Data indicate a measurable (5 Pa) influence of SVE on subslab/indoor pressure differential may occur but is not essential for effective VI mitigation. Indoor air quality improvements were evident in buildings 100 to 200 feet away from SVE including those without a measurable reversal of differential pressure across the slab or substantial reductions in subslab VOC concentration. These cases also demonstrated mitigation effects across a four-lane avenue with subsurface utilities. These findings suggest that SVE affects distant VI entry points with little observable impact on differential pressures and without relying on subslab VOC concentration reductions.

SUBSURFACE UTILITY NETWORK CADASTRE PROPOSAL, BASED ON LADM (ISO / FDIS 19152)

The increasing usage of the space above and underneath the land surface brings up the need of controlling its occupation. The localization of the infrastructure underground network is a major challenge, and for most of the countries, this information is not yet available or not easy to access. At this juncture, the cadastre of the network location is of fundamental importance for the water management supply systems. ISO 19152 from 2012 - Land Administration Domain Model (LADM) - addresses the standardization and integration of common features of cadastre systems in a simple way, and may be applied to several aspects of land administration. Within this context, this research aims to develop a model of subsurface water utility networks, based on the standard proposed by LADM. The implementation of the model suggested by ISO 19152 was tested using the COMPESA cadastre of the water utility network as a study case. The modernization and the technological advances adopted by the company facilitated the understanding of the geographical database underlying structure and its adaptation to the international standard. The modeling was produced using UML language, and DBDesigner for the physical implementation, executed in connection with PostgreSQL/PostGIS and QGIS, was applied.

Testing the horizontal to vertical spectral ratio technique as a tool for utility detection

In this study, experiments were conducted to explore the potential of utilizing the horizontal-to-vertical spectral ratio HVSR technique for detecting subsurface utilities. The hypothesis behind the adoption of this technique is that the presence of utility may produce impedance contrast that will affect the shape of the HVSR curve. Synthetic modeling and experiments revealed that the effect would be an extra spectral peak at the high-frequency end of the curve. The tests also aim towards the detection of the depth to the utility. An empirical formula was used to determine the depth, using the frequency of the additional peak and the shear wave velocity obtained from the Multichannel Analysis of Surface waves MASW measurements. Single-station and multi-receiver recordings were conducted at some localities at the Universiti Sains Malaysia main campus at Penang Island, northwest peninsular Malaysia. Areas of known subsurface utilities and the utility map, as provided by the development department of the university, were utilized to validate the results. The results obtained confirm the validity of the hypothesis, and a comparison of the obtained depths and locations of the utility shows an acceptable agreement.

Interpretation of Ground Penetrating Radar Dataset using Normalised Cross-Correlation Technique

Ground Penetrating Radar (GPR) is one of the latest non-destructive geophysical technology and most widely used in detecting underground utilities. GPR can detect both metal and non-metal, however, it is unable to identify the type of underground utility object. Many researchers come out with their techniques to interpret the GPR image. The current method requires experience in interpretation. Thus, in this study, a new method to detect underground utility utilizing the Normalised Cross-Correlation (NCC) template matching technique is proposed. This technique will reduce the dependency on experts to interpret the radargram, less time consuming and eventually save cost. Upon detection, the accuracy of the system is assessed. From the accuracy assessment performed, it is shown that the system provides accurate detection results for both, depth and pipe size. The Root Mean Square Error (RMSE) for the buried pipe depth obtained by using the proposed system is 0.110 m, whereas the highest percentage match obtained is 91.34%, the remaining 8.66% mismatched might be due to the soil condition, velocity or processing parameter that affected the radargram. Based on the assessment, the developed system seems capable to detect the subsurface utility if the radar image and template image used is acquired using the same antenna frequency, point interval, and similar GPR instrument.

A DATA CAPTURE FRAMEWORK FOR IMPROVING THE QUALITY OF SUBSURFACE UTILITY INFORMATION

Abstract. To plan, develop, and manage underground space and make informed decisions leading to desirable outcomes, planners, land administrators, and engineers need to make sense of the underground. A reliable digital twin of the underground – a realistic, digital representation of the physical world below the surface – is required. Utilities, consisting of the pipes, ducts, cables, manholes and other assets that provide electricity, gas, water, sewerage, and telecommunication services, make up a significant portion of the shallow layers of the subsurface. A lack of reliable information on subsurface utilities may potentially lead to poor decisions leading to undesirable outcomes, lengthy and costly planning and land administration processes, and lengthy, costly, and hazardous development processes. The advent of mobile 3D ground penetrating radar technology offers the promise of non-destructive mapping of existing subsurface utilities in a large area in a relatively short amount of time and could potentially be used to improve the reliability of available information. In this work, a small number of cases studies are described at evaluating the feasibility of a large area mapping approach for subsurface utilities. Based on the results of the studies, a data capture framework for the gradual improvement of the quality of information on existing subsurface utilities is proposed.

THE LADM-BASED 3D UNDERGROUND UTILITY MAPPING: CASE STUDY IN SINGAPORE

Abstract. A digital twin can be defined as a realistic representation of something physical. To improve decision making, a reliable digital twin of the underground is required. Utilities represent a significant portion of physical assets existing underground. Most of these utilities are situated in the relatively shallow layers of the underground, up to a few meters below the surface. The reliable information on subsurface utilities has clear benefits all throughout the life cycle of state land, resulting in efficient decision-making processes, cost savings, and additional revenues for land administration professionals. However, the lack of a reliable map of subsurface utilities lead to ill-informed decisions, costly information gathering, and missed business potential. This paper will share a case study of the 3D underground utility mapping workflow from data capture to usage in Singapore. The new data is collected by the Pegasus: Stream, a mobile mapping platform which captures above and underground data using the photo, laser and ground penetrating radar technology. Based on the Land Administration Domain Model (LADM) 3D underground utility data model, the new collected data is used with the existing utility and cadastral data for land administration. This study case expects to explore the reasonable workflow of 3D underground utility mapping to provide reliable information for land administration.

In situ stress states at KURT, an underground research laboratory in South Korea for the study of high-level radioactive waste disposal

The Korea Atomic Energy Research Institute Underground Research Tunnel (KURT) is an underground research laboratory in South Korea, built for investigations into the geological disposal of high-level radioactive waste. We characterize in situ stress states at KURT using data from a series of hydraulic fracturing (HF) tests and borehole image logs to depths of ~700 m in two boreholes. The tensile fractures induced by HF tests, plus several borehole stress indicators (e.g., drilling-induced tensile fractures and borehole breakouts) measured from image logs, consistently indicate an ESE–WNW oriented maximum horizontal principal compressive stress (SHmax). This site-scale SHmax orientation varies slightly from the regional-scale SHmax orientation, likely reflecting a local stress perturbation resulting from a fault network that traverses the site. Estimated magnitudes of the minimum horizontal principal compressive stress (Shmin), determined either from the shut-in pressures recorded during HF tests or from stress indicators on image logs, are comparable to the vertical stress, indicating that the stress regime at the KURT site straddles the boundary between strike-slip and reverse faulting. The depth-dependent trend in estimated SHmax magnitudes deviates at ~500 m depth, which we attribute to variations in the distribution of natural fractures in the granitic rock mass. This depth-dependent variation in SHmax magnitudes has implications for the slip stability of pre-existing fractures at the site. That is, at shallow depths, SHmax lies within the Coulomb stress limit for optimally oriented fractures and faults with a frictional coefficient of 0.6, whereas at greater depths, SHmax exceeds this limit, meaning that pre-existing fractures at shallow depth are more susceptible to slip reactivation. Our stress estimation results suggest that the site-scale stress state is strongly coupled with characteristics of natural fractures, emphasizing the importance of detailed geologic and stress data for subsurface utilization and its stability evaluation.

The New Lightweight Contender: Ultra-lightweight Foamed Glass Aggregate Finds the U.S. Market

Geotechnical engineers are often confronted with sites where the existing soil is prone to settlement or where there are subsurface utilities sensitive to additional load. If relocation or realignment aren’t options for the project, there are many resources in the geotechnical engineer’s toolbox that have traditionally been used to address these issues

Utilities of Virtual 3D City Models Based on CityGML: Various Use Cases

Virtual 3D city models are increasingly being used to model the realms of the real world for utilization in a number of applications related to environmental simulations including, urban planning, mapping the energy characteristics of buildings, noise mapping, flood modelling, etc. Apart from geometric and appearance/textural information, these applications have a requirement for complex urban semantics. Currently, a number of 3D standards are available in CAD, BIM and GIS related domains for the storage, visualization and transfer of 3D geospatial datasets. Initially, the 3D data models (such as COLLADA, VRML, X3D, etc.) were purely graphical/geometrical in nature and mainly used for visualization purposes. With the inclusion of thematic modules in OGC CityGML, the integration of geometry and semantics in a single data model paved the way for better sharing of virtual 3D city models. In spite of the availability of a wide range of 3D data standards, there are certain differences with respect to geometry, topology, semantics, LODs, etc., which complicates the integration of 3D geodata from heterogeneous sources. This paper serves to highlights the need for the innovative solutions with respect to the urban environmental related simulations primarily based on the use of virtual 3D city models. Four use cases are studied in this context namely, (1) urban solar potential estimation using CityGML models, (2) simulation of traffic noise level mapped on building walls from the urban road segments, (3) CityGML based 3D data models interoperability, and (4) 3D indoor logistics and subsurface utilities. However, for modelling majority of use cases, CityGML does not provide explicit thematic representations but provides support for extending the CityGML schema using Application Domain Extensions. In a nutshell, the study explores the semantic modelling capabilities of the CityGML for the transformation of native 3D virtual city models to one satisfying capabilities like semantic information and support towards interoperability.

An innovative computationally efficient hydromechanical coupling approach for fault reactivation in geological subsurface utilization

Estimating the efficiency and sustainability of geological subsurface utilization, i.e., Carbon Capture and Storage (CCS) requires an integrated risk assessment approach, considering the occurring coupled processes, beside others, the potential reactivation of existing faults. In this context, hydraulic and mechanical parameter uncertainties as well as different injection rates have to be considered and quantified to elaborate reliable environmental impact assessments. Consequently, the required sensitivity analyses consume significant computational time due to the high number of realizations that have to be carried out. Due to the high computational costs of two-way coupled simulations in large-scale 3D multiphase fluid flow systems, these are not applicable for the purpose of uncertainty and risk assessments.Hence, an innovative semi-analytical hydromechanical coupling approach for hydraulic fault reactivation will be introduced. This approach determines the void ratio evolution in representative fault elements using one preliminary base simulation, considering one model geometry and one set of hydromechanical parameters. The void ratio development is then approximated and related to one reference pressure at the base of the fault. The parametrization of the resulting functions is then directly implemented into a multiphase fluid flow simulator to carry out the semi-analytical coupling for the simulation of hydromechanical processes. Hereby, the iterative parameter exchange between the multiphase and mechanical simulators is omitted, since the update of porosity and permeability is controlled by one reference pore pressure at the fault base. The suggested procedure is capable to reduce the computational time required by coupled hydromechanical simulations of a multitude of injection rates by a factor of up to 15.