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CONTENTS
Volume 34, Number 1, July 2024
 

Abstract
Sensing technologies and computational methodologies play a crucial role in understanding the condition of civil structures and ensuring the safe use of these facilities in the field of measurement. This special issue journal primarily features papers that introduce the latest smart measurement technologies, which were presented at "The 2024 World Congress on Advances in Civil, Environmental & Materials Research (ACEM24) / The 2024 Structures Congress (Structures24)," held in Seoul from August 19 to 22, 2024. The selected papers focus particularly on applications in the field of geotechnical engineering.

This special issue journal comprises eight papers, organized around three primary themes: 1) Advanced Sensing Technologies for Soil and Structural Analysis, 2) Computational Techniques for Predictive Monitoring and Data Analysis, and 3) Integrated Approaches in Environmental and Infrastructure Monitoring. The first theme presents the latest sensing techniques designed to understand the behavior of soil materials and biopolymer solutions, highlighting their potential applications. The second theme explores the application of a Multilayer Neural Network for assessing the integrity of rock bolts and discusses the use of oversampling algorithms. Furthermore, this theme investigates eXplainable AI techniques that utilize the waveform of time domain reflectometry to identify key influencing factors. The final theme includes temperature measurement techniques using Fiber Bragg Gratings sensors to validate the effects of geothermal energy. Additionally, this section covers a smart instrumented dynamic penetrometer, which is capable of measuring and analyzing various geotechnical properties. Notably, this paper was also presented as a keynote lecture during the main events of the ACEM24/Structures24 conference.

The research presented in this special issue is closely aligned with the interdisciplinary approaches and innovative solutions highlighted in the Smart Structures and Systems journal. These studies are intended to serve as a valuable reference for researchers engaged in cutting-edge work, with the goal of fostering more advanced and promising developments in the field.

Key Words


Address


Abstract
Structural health monitoring (SHM) of concrete structures is necessary because structural safety is directly linked to life safety. This study proposes a transmission line configuration for SHM based on time domain reflectometry (TDR). For thispurpose, six transmission lines consisting of electrical wires, rebars, and joints were prepared. The TDR waveforms were measured and analyzed in air and concrete using six transmission lines to select the most suitable configuration. A two-line wire with joints was selected as the optimal transmission line for SHM because it exhibited the highest sensitivity among the configurations. Experiments to apply SHM were performed on defective concrete blocks containing an optimal transmission line. The results showed that the defect locations in concrete were precisely investigated using TDR waveform analysis. The distances estimated from the TDR waveform were similar to the measured distances for the locations of the defects and joints in the concrete blocks. This study suggests that a transmission line consisting of two-line wires and joints may be an effective nondestructive evaluation tool for assessing the structural health of concrete.

Key Words
electromagnetic waves; non-destructive evaluation; structural health monitoring; time domain reflectometry; transmission line

Address
(1) Dongsoo Lee, Dong-Ju Kim, Jinwook Kim, Jong-Sub Lee:
School of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea;
(2) Sang Yeob Kim:
Department of Fire and Disaster Prevention, Konkuk University, 268, Chungwon-daero, Chungju-si, Chungcheongbuk-do, 27478, Republic of Korea.

Abstract
Oversampling algorithms are methods employed in the field of machine learning to address the constraints associated with data quantity. This study aimed to explore the variations in reliability as data volume is progressively increased through the use of oversampling algorithms. For this purpose, the synthetic minority oversampling technique (SMOTE) and the borderline synthetic minority oversampling technique (BSMOTE) are chosen. The data inputs, which included air temperature, humidity, and wind speed, are parameters used in the Fosberg Fire-Weather Index (FFWI). Starting with a base of 52 entries, new data sets are generated by incrementally increasing the data volume by 10% up to a total increase of 100%. This augmented data is then utilized to predict FFWI using a deep neural network. The coefficient of determination (R2) is calculated for predictions made with both the original and the augmented datasets. Suggesting that increasing data volume by more than 50% of the original dataset quantity yields more reliable outcomes. This study introduces a methodology to alleviate the challenge of establishing a standard for data augmentation when employing oversampling algorithms, as well as a means to assess reliability.

Key Words
Borderline Synthetic Minority Oversampling TEchnique (BSMOTE); Deep neural network (DNN); Fosberg Fire Weather Index (FFWI); oversampling algorithm; Synthetic Minority Oversampling TEchnique (SMOTE)

Address
(1) Sang Yeob Kim:
Department of Fire and Disaster Prevention, Konkuk University, 268, Chungwon-daero, Chungju-si, Chungcheongbuk-do, 27478, Republic of Korea;
(2) Dongsoo Lee:
School of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea;
(3) Jung-Doung Yu:
Department of Civil Engineering, Joongbu University, Goyang, 10279, Republic of Korea;
(4) Hyung-Koo Yoon:
Department of Construction and Disaster Prevention Engineering, Daejeon University, 62, Daehak-ro, Dong-gu, Daejeon, 34520, Republic of Korea.

Abstract
Non-destructive methods are extensively utilized for assessing the integrity of rock bolts, with longitudinal wave speed being a crucial property for evaluating rock bolt quality. This research aims to propose a method for predicting reliable longitudinal wave velocities by leveraging various properties of the rock surrounding the rock bolt. The prediction algorithm employed is the Multilayer Neural Network (MNN), and the input properties includes elastic modulus, shear wave speed, compressive strength, compressional wave speed, mass density, porosity, and Poisson's ratio, totaling seven. The implementation of the MNN demonstrates high reliability, achieving a coefficient of determination of 0.996. To assess the impact of each input property on longitudinal wave speed, an importance score is derived using the random forest algorithm, with the elastic modulus identified as having the most significant influence. When the elastic modulus is the sole input parameter, the coefficient of determination for predicting the longitudinal wave speed is observed to be 0.967. The findings of this study underscore the reliability of selecting specific properties for predicting longitudinal wave speed and suggest that these insights can assist in identifying relevant input properties for rock bolt integrity assessments in future construction site experiments.

Key Words
experiment; longitudinal wave speed; Multilayer Neural Network (MNN); rock bolt

Address
(1) Jung-Doung Yu:
Department of Civil Engineering, Joongbu University, Goyang, 10279, Republic of Korea;
(2) Geunwoo Park, Dong-Ju Kim:
School of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea;
(3) Hyung-Koo Yoon:
Department of Construction and Disaster Prevention Engineering, Daejeon University, Daejeon, 34520, Republic of Korea.

Abstract
As materials cure, the internal electrical flow changes, leading to variations in the dielectric constant over time. This study aims to assess the impact of voltage values extracted from time domain reflectometry (TDR) waveforms, measured during the curing of materials, on predicting the dielectric constant. The experiments are conducted over a curing period ranging from 60 to 8640 minutes, with 30 TDR trials. From the measured waveforms, values of V0, V1, V2, Vf, and Δt are deduced. Additionally, curing time is included as an input variable. Groups A and B are distinguished based on the presence or absence of

Key Words
curing time; feature contribution; partial dependence (PD) algorithm; SHapley Additive exPlanations (SHAP) algorithm; time domain reflectometry (TDR)

Address
(1) Won-Taek Hong:
Department of Civil & Environmental Engineering, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, 13120, Republic of Korea;
(2) WooJin Han:
School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea;
(3) Yong-Hoon Byun:
Department of Agricultural Civil Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 41566, Republic of Korea;
(4) Hyung-Koo Yoon:
Department of Construction and Disaster Prevention Engineering, Daejeon University, 62, Daehak-ro, Dong-gu, Daejeon, 34520, Republic of Korea.

Abstract
We aimed to assess the evolution of small-strain stiffness and relative density in non-compacted embankment layers. We developed embedded type in-situ soil stiffness measurement devices for monitoring small-strain stiffness occurring after filling at a test site and conducted comprehensive laboratory compaction tests using an oedometer cell with a bender element. However, direct comparison is extremely difficult because the shear wave velocity measured in the field and laboratory depend on depth and effective stress, respectively. Therefore, we propose a method for establishing a relationship between effective stress and depth using a compressibility model. In this study, the shear wave velocity measured in the field was compared to the estimated shear wave velocity-depth profiles for completely dry and saturated conditions with different relative densities. The relative density under saturated soil conditions may vary between 50% and 90% and tends to be closer to 95%. Under dry soil conditions, the relative density of the embankment can vary from 30% to 70% and tends to approach 76%. For model validation, the relative density estimated from shear wave velocity-depth profiles was compared to that estimated from DCPI data. In other words, the results analyzed in the context of an effective stress-depth model enable the prediction of engineering properties such as the small-strain stiffness and relative density of embankment layers. This study demonstrates that physics-based data analyses successfully capture the relative density of non-compacted embankment layers.

Key Words
compressibility model; embedded type in-situ soil stiffness measurement device; non-compacted embankment layer; relative density; small-strain stiffness

Address
(1) Namsun Kim, Jong-Sub Lee, Younggeun Yoo, Jinwook Kim:
School of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea;
(2) Junghee Park:
Department of Civil and Environmental Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea.

Abstract
The accurate measurement of temperature in the ground source heat pump system is crucial for assessing the thermal response of the system and validating the numerical model for parametric study, which is necessary for the thermal performance evaluation of the geothermal energy system. Conventional temperature sensors have some disadvantages such as they are difficult to install, and their position can be shifted during the backfill process of the ground heat exchanger. In this study, Fiber Bragg Grating (FBG) sensors were used to measure the temperature change of a recently developed ground heat exchanger (Coil Column Unit, CCU). FBG sensors were first calibrated in a thermal chamber alongside a correlation sensor (RTD sensor). The calibrated sensors were then mounted on the pipe surface at each spiral of the CCU to measure how temperature changes during the in-door mockup thermal response test. Finally, the measurement results of the FBG sensors were verified with a finite element coded program. The results indicated that the temperature difference between the numerical analysis and the experiment was less than 1%, which is significantly lower than that of the previous study using the RTD sensors. Therefore, it is feasible to apply FBG sensors for temperature measurement during the operation of the TRT of the geothermal energy system.

Key Words
coil column unit; FBG sensor; geothermal energy; ground heat exchanger; temperature measurement; thermal response test

Address
(1) Young-Sang Kim, Duc-Thang Hoang, Ba Huu Dinh:
Department of Civil Engineering, Chonnam National University, Yongbong-ro 77, Buk-gu, Gwangju, 61186, South Korea;
(2) Gyeong-O Kang:
Department of Civil Engineering, Gwangju University, 277 Hyodeong-ro, Nam-gu, Gwangju, South Korea;
(3) Ba Huu Dinh:
Laboratory for Computational Civil Engineering, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam;
(4) Ba Huu Dinh:
Faculty of Civil Engineering, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam.

Abstract
Application of biopolymers to improve the mechanical properties of soils has been extensively reported. However, a comprehensive understanding of various engineering applications is necessary to enhance their effectiveness. While numerous experimental studies have investigated the use of biopolymers as injection materials, a detailed understanding of their injection behavior in soil through numerical analyses is lacking. This study aimed to address this gap by employing pore network modeling techniques to analyze the injection characteristics of biopolymer solutions in soil. A pore network was constructed from computed tomography images of Ottawa 20-30 sand. Fluid flow simulations incorporated power-law parameters and governing equations to account for the viscosity characteristics of biopolymers. Agar gum was selected as the biopolymer for analysis, and its injection characteristics were evaluated in terms of concentration and pore-size distribution. Results indicate that the viscosity properties of biopolymer solutions significantly influence the injection characteristics, particularly concerning concentration and injection pressure. Furthermore, notable trends in injection characteristics were observed based on pore size and distribution. Importantly, in contrast to previous studies, meaningful correlations were established between the viscosity of the injected fluid, injection pressure, and injection distance. Thus, this study introduces a novel methodology for integrating pore network construction and fluid flow characteristics into biopolymer injections, with potential applications in optimizing field injections such as permeation grouting.

Key Words
biopolymer injection; permeation grouting; pore network model; pore-size distribution; shear-thinning fluid flow

Address
(1) Jae-Eun Ryou, Beomjoo Yang, Jongwon Jung:
School of Civil Engineering, Chungbuk National University, 1, Chungdae-ro, Seowon-gu, Cheongju-si, Chungcheongbuk-do, Republic of Korea;
(2) Won-Taek Hong:
Department of Civil Environmental Engineering, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea.

Abstract
In-situ penetration tests using dynamic penetrometers are widely used for estimating soil resistance. Additionally, these dynamic penetrometers have been instrumented to improve tests accuracy. This paper introduces smart instrumented dynamic penetrometers and discusses experimental studies for various cases. An energy monitoring module was developed to enhance the dynamic penetration tests. The standard penetration test (SPT) and instrumented dynamic cone penetrometer (IDCP) tests were conducted using the energy monitoring module. Dynamic responses obtained by the energy monitoring module were used to calculate the transferred energies into the rod head and tip to correct the evaluation of ground strength. In addition, a crosshole-type dynamic penetrometer (CDP) was developed to measure the penetration index and shear wave velocity simultaneously to estimate the strength and stiffness of ground. The results of this study indicate that smart instrumented dynamic penetrometers may be effectively used to characterize the strength and stiffness of ground.

Key Words
Crosshole-type Dynamic Penetrometer (CDP); energy monitoring module; Instrumented Dynamic Cone Penetrometer (IDCP); Standard Penetration Test (SPT); Transferred energy

Address
(1) Geunwoo Park, Namsun Kim, Jong-Sub Lee:
School of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea;
(2) Yong-Hoon Byun:
Department of Agricultural Civil Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, 41566, Republic of Korea;
(3) Sang Yeob Kim:
Department of Fire and Disaster Prevention, Konkuk University, 268, Chungwon-daero, Chungju-si, Chungcheongbuk-do, 27478, Republic of Korea.

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