Techno Press
Tp_Editing System.E (TES.E)
Login Search
You logged in as

Volume 32, Number 1, July 2023

Our friend and colleague, Dr. Shih-Chi Liu, passed away on June 30, 2023. Born in China, Dr. Liu grew up in Taiwan and received his undergraduate degree from National Taiwan University before moving to the University of California, Berkeley to pursue his PhD degree under the supervision of Prof. Joseph Penzien. Upon graduation, Dr. Liu worked for eight fruitful years at Bell Laboratories focusing on the emerging area of probabilistic methods applied to seismic risk analysis. He joined the National Science Foundation as a Program Director in 1975, where he showed great foresight and superb intuition regarding the most important areas of study for earthquake engineering and disaster mitigation. He was an exceptional administrator who understood the intricacies of the research program that he directed. He had the unique skill of keeping up with his primary researchers and understanding what they were attempting to accomplish. Even more so, he was able to see beyond the immediate goals of their research and guide their work in fruitful directions. As a result, he was uniquely successful, not only in laying the groundwork and drawing the blueprint for large-scale research efforts, but also in seeing his visions come to fruition. Moreover, Dr. Liu was a true visionary in recognizing the synergy of multinational research cooperation and a pioneer in the planning, development, implementation, and support of such cooperative research programs, especially between the United States and countries in the Pacific Rim and Europe. Indeed, Dr. Liu was known as a charismatic and creative leader, leveraging multilateral collaborations to enhance the safety of our society and the reliability of civil infrastructure, and fostering understanding and goodwill among engineers and scientists throughout the world. For example, following the Tangshan Earthquake (1976), Dr. Liu catalyzed over 20 years of USA-China collaboration in earthquake engineering. Additionally, in the wake of the Northridge (1994) and Kobe (1995) earthquakes, Dr. Liu spearheaded efforts between the NSF/USA and Monbusho/Japan to initiate a major 5-year US-Japan cooperative research program on earthquake disaster mitigation research, through which performance-based seismic design and engineering methods and other related advanced technologies were brought into focus for the first time. He also led the way, through multidisciplinary team and center-based research, toward the development and utilization of smart structures technology, including structural control and health monitoring, to establish the knowledge and technological bases enabling smart buildings and other structures. The impact of his tireless efforts stands as lasting testimony to Dr. Liu's insight and wisdom. He is survived by his wife, Rae, two daughters, Libby and Debbie, and three grandchildren.

Key Words


In modal analysis, the mode shape reflects the vibration characteristics of the structure, and thus it is widely performed for finite element model updating and structural health monitoring. Generally, the acceleration-based mode shape is suitable to express the characteristics of structures for the translational vibration; however, it is difficult to represent the rotational mode at boundary conditions. A tilt sensor and gyroscope capable of measuring rotational mode are used to analyze the overall behavior of the structure, but extracting its mode shape is the major challenge under the small vibration always. Herein, we conducted a feasibility study on a multi-mode shape estimating approach utilizing a single physical quantity signal. The basic concept of the proposed method is to receive multi-metric dynamic responses from two sensors and obtain mode shapes through bridge loading test with relatively large deformation. In addition, the linear transformation matrix for estimating two mode shapes is derived, and the mode shape based on the gyro sensor data is obtained by acceleration response using ambient vibration. Because the structure's behavior with respect to translational and rotational mode can be confirmed, the proposed method can obtain the total response of the structure considering boundary conditions. To verify the feasibility of the proposed method, we pre-measured dynamic data acquired from five accelerometers and five gyro sensors in a lab-scale test considering bridge structures, and obtained a linear transformation matrix for estimating the multi-mode shapes. In addition, the mode shapes for two physical quantities could be extracted by using only the acceleration data. Finally, the mode shapes estimated by the proposed method were compared with the mode shapes obtained from the two sensors. This study confirmed the applicability of the multi-mode shape estimation approach for accurate damage assessment using multi-dimensional mode shapes of bridge structures, and can be used to evaluate the behavior of structures under ambient vibration.

Key Words
angular velocity; linear transformation matrix; modal analysis

"(1) Seung-Hun Sung:
Agency for Defense Development, Yuseong, P.O. Box 35, Daejeon, 34186 South Korea;
(2) Gil-Yong Lee:
Department of Mechanical Engineering, KAIST, Daejeon 34141, South Korea;
(3) In-Ho Kim:
Department of Civil Engineering, Kunsan National University, Gunsan 54150, South Korea."

The parameters of civil engineering structures have time-variant characteristics during their service. When extremely large external excitations, such as earthquake excitation to buildings or overweight vehicles to bridges, apply to structures, sudden or gradual damage may be caused. It is crucially necessary to detect the occurrence time and severity of the damage. The unscented Kalman filter (UKF), as one efficient estimator, is usually used to conduct the recursive identification of parameters. However, the conventional UKF algorithm has a weak tracking ability for time-variant structural parameters. To improve the identification ability of time-variant parameters, an adaptive UKF with forgetting factor (AUKF-FF) algorithm, in which the state covariance, innovation covariance and cross covariance are updated simultaneously with the help of the forgetting factor, is proposed. To verify the effectiveness of the method, this paper conducted two case studies as follows: the identification of time-variant parameters of a simply supported bridge when the vehicle passing, and the model updating of a sixstory concrete frame structure with field test during the Yangbi earthquake excitation in Yunnan Province, China. The comparison results of the numerical studies show that the proposed method is superior to the conventional UKF algorithm for the time-variant parameter identification in convergence speed, accuracy and adaptability to the sampling frequency. The field test studies demonstrate that the proposed method can provide suggestions for solving practical problems.

Key Words
adaptive tracking; forgetting factor; model updating; state variable; time-variant parameters

"(1) Yanzhe Zhang, Jianqing Bu:
State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;
(2) Yanzhe Zhang:
School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;
(3) Yong Ding:
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China;
(4) Yong Ding:
Key Lab of Structures Dynamic Behavior and Control (Harbin Institute of Technology), Ministry of Education, Heilongjiang, Harbin 150090, China;
(5) Jianqing Bu:
School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;
(6) Jianqing Bu:
Key Laboratory of traffic safety and control of Hebei Province, China;
(7) Lina Guo:
College of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150048, China."

Steel bolted joint is an important part of steel structure, and its damage directly affects the bearing capacity and durability of steel structure. Currently, the existing research mainly focuses on the identification of corroded bolts and corroded bolts respectively, and there are few studies on multiple states. A detection framework of corroded and loosened bolts is proposed in this study, and the innovations can be summarized as follows: (i) Vision Transformer (ViT) is introduced to replace the third and fourth C3 module of you-only-look-once version 5s (YOLOv5s) algorithm, which increases the attention weights of feature channels and the feature extraction capability. (ii) Three states of the steel bolts are considered, including corroded bolt, bolt missing and clean bolt. (iii) Line segment detector (LSD) is introduced for bolt rotation angle calculation, which realizes bolt looseness detection. The improved YOLOv5s model was validated on the dataset, and the mean average precision (mAP) was increased from 0.902 to 0.952. In terms of a lab-scale joint, the performance of the LSD algorithm and the Hough transform was compared from different perspective angles. The error value of bolt loosening angle of the LSD algorithm is controlled within 1.09%, less than 8.91% of the Hough transform. Furthermore, the proposed framework was applied to fullscale joints of a steel bridge in China. Synthetic images of loosened bolts were successfully identified and the multiple states were well detected. Therefore, the proposed framework can be alternative of monitoring steel bolted joints for management department.

Key Words
"corroded and loosened bolt detection; improved YOLOv5s; linear segment detector; steel bolted joints; vision transformer"

"(1) Youhao Ni, Jianxiao Mao, Hao Wang, Zhuo Xi:
Key Laboratory of C&PC Structures of Ministry of Education, Southeast University, Nanjing, 211189, China;
(2) Youhao Ni, Jianxiao Mao, Hao Wang, Zhuo Xi:
School of Civil Engineering, Southeast University, Nanjing 211889, China;
(3) Yuguang Fu:
School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore."

Indirect determination of pan evaporation (PE) has been highly regarded, due to the advantages of intelligent models employed for this objective. This work pursues improving the reliability of a popular intelligent model, namely multi-layer perceptron (MLP) through surmounting its computational knots. Available climatic data of Fresno weather station (California, USA) is used for this study. In the first step, testing several most common trainers of the MLP revealed the superiority of the Levenberg-Marquardt (LM) algorithm. It, therefore, is considered as the classical training approach. Next, the optimum configurations of two metaheuristic algorithms, namely cuttlefish optimization algorithm (CFOA) and teaching-learning-based optimization (TLBO) are incorporated to optimally train the MLP. In these two models, the LM is replaced with metaheuristic strategies. Overall, the results demonstrated the high competency of the MLP (correlations above 0.997) in the presence of all three strategies. It was also observed that the TLBO enhances the learning and prediction accuracy of the classical MLP (by nearly 7.7% and 9.2%, respectively), while the CFOA performed weaker than LM. Moreover, a comparison between the efficiency of the used metaheuristic optimizers showed that the TLBO is a more time-effective technique for predicting the PE. Hence, it can serve as a promising approach for indirect PE analysis.

Key Words
environmental management; multi-layer perceptron; pan evaporation; teaching-learning-based optimization

"(1) Rana Muhammad Adnan Ikram:
School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China;
(2) Imran Khan:
Department of Economics, The University of Haripur, Pakistan;
(3) Hossein Moayedi, Loke Kok Foong, Binh Nguyen Le:
Institute of Research and Development, Duy Tan University, Da Nang, Vietnam;
(4) Hossein Moayedi, Loke Kok Foong, Binh Nguyen Le:
School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam."

A bridge transportation network supplies products from various source nodes to destination nodes through bridge structures in a target region. However, recent frequent earthquakes have caused damage to bridge structures, resulting in extreme direct damage to the target area as well as indirect damage to other lifeline structures. Therefore, in this study, a surrogate modelbased comprehensive framework to estimate the seismic resilience of bridge transportation networks is proposed. For this purpose, total system travel time (TSTT) is introduced for accurate performance indicator of the bridge transportation network, and an artificial neural network (ANN)-based surrogate model is constructed to reduce traffic analysis time for high-dimensional TSTT computation. The proposed framework includes procedures for constructing an ANN-based surrogate model to accelerate network performance computation, as well as conventional procedures such as direct Monte Carlo simulation (MCS) calculation and bridge restoration calculation. To demonstrate the proposed framework, Pohang bridge transportation network is reconstructed based on geographic information system (GIS) data, and an ANN model is constructed with the damage states of the transportation network and TSTT using the representative earthquake epicenter in the target area. For obtaining the seismic resilience curve of the Pohang region, five epicenters are considered, with earthquake magnitudes 6.0 to 8.0, and the direct and indirect damages of the bridge transportation network are evaluated. Thus, it is concluded that the proposed surrogate modelbased framework can efficiently evaluate the seismic resilience of a high-dimensional bridge transportation network, and also it can be used for decision-making to minimize damage.

Key Words
artificial neural network; bridge transportation network; seismic resilience; surrogate model; total system travel time

"(1) Sungsik Yoon:
Department of Artificial Intelligence, Hannam University, 70 Hannam-ro, Daedeok-Gu, Daejeon 34430, Republic of Korea;
(2) Young-Joo Lee:
Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea."

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

Key Words
active learning; ensemble of surrogate; model updating; probabilistic ensemble; TMCMC

"(1) Guangwei Lin, Yi Zhang, Enjian Cai, Taisen Zhao, Zhaoyan Li:
Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, China;
(2) Guangwei Lin, Yi Zhang, Enjian Cai, Taisen Zhao, Zhaoyan Li:
Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, China;
(3) Guangwei Lin, Yi Zhang, Enjian Cai, Taisen Zhao:
Department of Civil Engineering, Tsinghua University, Beijing, China.

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2024 Techno-Press ALL RIGHTS RESERVED.
P.O. Box 33, Yuseong, Daejeon 34186 Korea, Email: