State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction

Jong, S.C.; Ong, Danny; Oh, Erwin

doi:10.1016/j.tust.2021.103946

Cited by 73 publications

(18 citation statements)

References 203 publications

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“…It computes the evolution of probability in the parameters, before observing the data, and after incorporating the data in the analysis [131]. It is found to be superior in updating, for example, soil parameters [132,133] or, in this case, surface roughness from prior studies, because it considers these parameters as random variables instead of fixed constants. Thus, the Bayesian updating process provides a robust approach in back analyzing or predicting soil parameters based on the field observational method compared to deterministic techniques that produce outputs with fixed values [134].…”

Section: Bayesian Methodsmentioning

confidence: 99%

Influence of Surface Roughness and Particle Characteristics on Soil–Structure Interactions: A State-of-the-Art Review

et al. 2022

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The study of soil–structure interface behavior contributes to the fundamental understanding of engineering performance and foundation design optimization. Previous research studies the effect of soil characteristics and surface roughness property on the soil–material interface mechanism via interface shear test. The reviews utilizing past established laboratory studies and more recent tests based on state-of-the-art technologies reveal that surface roughness significantly affects interface shear performances in the studies of soil–structure interactions, especially in peak shear strength development. A preliminary but original investigative study by the authors was also carried out using a sophisticated portable surface roughness gauge to define the material surface roughness properties in order to study the interface behavior parametrically. Additionally, using the authors’ own original research findings as a proof-of-concept innovation, particle image velocimetry (PIV) technology is applied using a digital single-lens reflex (DSLR) camera to capture sequential images of particle interactions in a custom-built transparent shear box, which validate the well-established four-stage soil shearing model. The authors also envisaged that machine learning, e.g., artificial neural network (ANN) and Bayesian inference method, amongst others, as well as numerical modeling, e.g., discrete element method (DEM), have the potential to also promote research advances on interface shear mechanisms, which will assist in developing a greater understanding in the complex study of soil–structure interactions.

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Section: Bayesian Methodsmentioning

confidence: 99%

Influence of Surface Roughness and Particle Characteristics on Soil–Structure Interactions: A State-of-the-Art Review

et al. 2022

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“…In addition, because of the recent expansion in the construction industry and usage of information technology, which has provided possibilities such as data collection and analysis, artificial intelligence (AI) algorithms have been used to address complex engineering problems [8]. However, to collect data from jobsites, current productivity evaluation methods require an information system that can analyze and process the collected data [9].…”

Section: B) Global Navigation Satellite System (Gnss)mentioning

confidence: 99%

“…Other than that, the high cost of critical gears, such as advanced excavators, cranes, and trucks, makes the industry more traditional [7]. In contrast, digital technologies such as artificial intelligence, big data, machine learning, and the Internet of Things (IoTs) can improve the productivity of heavy-duty machinery operations owing to data-driven methodologies [8]. The urge to improve productivity and safety is driving the adoption of IoTs in construction.…”

Section: Introductionmentioning

confidence: 99%

“…The urge to improve productivity and safety is driving the adoption of IoTs in construction. IoTs allows real-time connectivity between a computer platform and actual construction sites [5], which can provide not only real-time monitoring and identification of heavy machinery for integrated fleet management and resource allocation, which is essential [10], but also to measure performance, analyze workflow, and reduce equipment-related injuries [8]. Further, the market for IoTs in construction was valued at USD 7.8 billion in 2019 and is expected to grow at a CAGR of 16.5 percent to USD 16.8 billion by 2024 [9].…”

Section: Introductionmentioning

confidence: 99%

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Overview of Emerging Technologies for Improving the Performance of Heavy-Duty Construction Machines

et al. 2022

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Construction equipment is one of the most significant resources in every large construction project, accounting for a considerable portion of the project budget. Improving heavy machinery performance can increase efficiency and reduce costs. However, research on boosting machine efficiency is limited. This study adopts mix review methodology (systematic review and bibliometric analysis) and evaluates emerging technologies like Digital twin, Cyber physical systems, Geo-graphic information systems, Global navigation satellite system, Onboard instrumentation system, Radio frequency identification, Internet of things, Telematics, Machine learning, Deep learning, and Computer vision for machine productivity and provides insights into how they can be used to improve the performance of construction equipment. The article defined three major equipment operating areas-monitoring and control, tracking and navigation, and data-driven performance optimization-classified technologies respectively, and explored how they can increase machine productivity. Other circumstantial issues affecting machine operation as well as loopholes in existing innovative tools used in machine processes were also highlighted. This study contributes to the goals of deploying digital tools and outlines future directions for the development of automated machines to optimize project efficiency.

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“…In this study, simple regression methods, which are based on ordinary least squares, and an advanced regression technique, which is based on Gaussian process, were employed for the UCS prediction, considering additive content (A) and curing period (T) for the UCS of both lime-and GG-stabilized soils. The choice of these supervised prediction models is rationally made based on reported recommendation for geotechnical application (Jong et al 2021) and is further explicated in the ensuing sections. The summary of the steps required for each of these methods is provided thus:…”

Section: Predictive Modelingmentioning

confidence: 99%

Feasibility of lime and biopolymer treatment for soft clay improvement: a comparative and complementary approach

2022

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Highway pavement infrastructure projects which involve soil improvement should be executed, ensuring environmental sustainability. In the present study, guar gum (GG) and lime were assessed for the purpose of soft clay stabilization. The experimental program for the soil stabilization employed a two-stage process. The initial stage involves treatment of the soil with various percentages of lime (3, 5, 7, and 9%) and GG (0.6, 1.0, 1.4, and 1.8%), maintaining the same material acquisition cost and considering curing (0, 7, 14, and 28 days) for the unconfined compressive strength (UCS). In the second experimental stage, a complementary approach in which 3% lime was combined with GG at various percentages (0.1, 0.2, and 0.3%) was employed. The tests conducted include UCS, California bearing ratio (CBR), and strength loss resistance (SLR). Results show that the sole use of lime and GG resulted in significant improvement in the UCS, albeit lime was better. While UCS improved with curing time for the lime-stabilized soil, UCS gain for GG occurred only for up to 7 days curing because biodegradation of GG by microbes in the soil ensues on further curing. Lime-GG stabilization resulted in better UCS and CBR improvement with curing than lime stabilization; however, lime stabilization yielded better SLR. The optimum additive content for strength improvement was obtained at 3% lime + 0.3% GG. Microstructural analysis indicated cementation in the stabilized soil. Predictive models for the UCS were developed based on regression methods. Model evaluation revealed that Gaussian process model provided the best UCS prediction.

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State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction

Cited by 73 publications

References 203 publications

Influence of Surface Roughness and Particle Characteristics on Soil–Structure Interactions: A State-of-the-Art Review

Influence of Surface Roughness and Particle Characteristics on Soil–Structure Interactions: A State-of-the-Art Review

Overview of Emerging Technologies for Improving the Performance of Heavy-Duty Construction Machines

Feasibility of lime and biopolymer treatment for soft clay improvement: a comparative and complementary approach

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