Abstract
Sustainable concrete mix design requires balancing mechanical performance with environmental and resource objectives, a multi-objective problem traditionally addressed through heuristic or trial-and-error methods. This study presents a surrogate-assisted multi-objective genetic optimization framework to model sustainable concrete design as a multi-player game, where compressive strength, CO2 emissions, energy consumption, and material usage are treated as rational players. Random Forest regressors serve as surrogate models for each objective, enabling efficient exploration of the design space. Two optimization approaches are compared: a weighted-fitness genetic algorithm (GA) and a NSGA-II-based multi-objective NSGA-II knee-point optimization with knee-point detection. The GA produces a compromise mix emphasizing strength, but at the cost of environmental and resource objectives, achieving 141.3 kg/cm2 strength, 433.1 kg CO2, and 2373.7 kWh energy. In contrast, the NSGA identifies a Pareto-optimal knee-point mix yielding 478.7 kg/cm2 strength, 247.5 kg CO2, and 1262.2 kWh energy, demonstrating substantial improvements across all objectives. The results highlight the superiority of multi-objective optimization in achieving Nash-type Pareto compromise solution for sustainable concrete design, offering a principled and interpretable framework for high-performance, eco-efficient mixes.
Key Words
CO2 Emission; Environment; Game Theory; Genetic Algorithm (GA); Multi-Objectives Optimization; Nash-Type Compromise Solution; Nsga-ii; Random Forest (RF); Sustainable Concrete Mixture
Address
(1) Razan Haedar Al Marahla:
Civil Engineering Department, Al-Zaytoonah University of Jordan, Amman, Jordan;
(2) Mohammad Zakaria Masoud:
Electrical Engineering Department, Al-Zaytoonah University of Jordan, Amman, Jordan;
(3) Ayman Mohammad Nasir:
Mechanical Engineering Department, Al-Zaytoonah University of Jordan, Amman, Jordan.
Abstract
Since traditional methods for detecting surface bubble defects usually suffer from drawbacks such as slow speed and low accuracy, a new You Only Look Once version 5s (YOLOv5s) model, incorporating Slim-neck and simple parameter-free attention module (SimAM), is proposed to address this challenge. Initially, data augmentation strategies are employed to expand the original dataset, which comprises 1,835 images, into a new dataset of 7,340 photographs, thereby enhancing the generalizability of the newly proposed YOLOv5s model. Subsequently, a lightweight Slim-Neck network, grounded in a group shuffling convolution (GSConv) module, is integrated into the backbone network to reduce the complexity of the newly proposed YOLOv5s model while maintaining a high level of target detection accuracy. Furthermore, a SimAM mechanism is embedded within the neck network, enabling the refined model to focus more intensively on the key characteristics of surface bubbles in fair-faced concrete. To validate the effectiveness and accuracy of the proposed YOLOv5s model, a dataset including 7,340 bubble defect images is investigated. The results demonstrate that the proposed model successfully strikes a commendable balance between the detection speed and accuracy. Additionally, it offers a promising and effective tool for detecting surface bubbles in real-world fair-faced concrete structures.
Address
(1) Shi-Han Xu, Jing-Liang Liu:
College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China;
(2) Shi-Han Xu:
College of Civil Engineering, Fuzhou University, Fuzhou 350108, China;
(3) Wen-Ting Zheng:
College of Civil Engineering, Fujian University of Technology, Fuzhou 350118, China;
(4) Zi-Xuan Chen:
Faculty of Science and Technology, University of Macau, Macau, China.
Abstract
Accurately predicting future bridge traffic states is crucial for intelligent transportation systems and structural health monitoring. However, this task still faces two major challenges. First, traffic data often suffer from sparsity or missing values due to equipment failures, limited sampling frequency, and transmission interference. Second, most existing methods rely on deterministic point estimates, which fail to quantify the inherent uncertainty in traffic states. To address these issues, this paper proposes AMU-TrafficNet (Attention-enhanced multi-scale uncertainty-aware traffic state network), an uncertainty‑aware hybrid model designed to predict bridge traffic states under realistic missing traffic data conditions. The model innovatively integrates multi-scale feature extraction, attention mechanisms, and uncertainty quantification. By incorporating task‑specific loss functions, it achieves the dual capability of reconstructing missing traffic data and providing uncertainty-aware future traffic state predictions. A proof-of-concept case study on an in-service cable-stayed bridge is conducted to demonstrate the feasibility of the proposed method. The results show promising performance in reconstructing missing data and predicting future traffic states within the scope of this specific case. Furthermore, this study examines the effects of input types, data scales, and hyperparameters on the results, conducts ablation studies, and compares the proposed model with several conventional models to validate its effectiveness.
Key Words
bridges; future traffic state prediction; machine learning; missing traffic data reconstruction; uncertainty quantification
Address
(1) Zengpeng Zhang, Xiang-Yu Wang, Da-Wei Lin, Zhen Sun:
School of Civil Engineering, Southeast University, Nanjing 211189, China;
(2) Yuguang Fu:
School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore;
(3) Fan Kong:
School of Civil Engineering, Hefei University of Technology, Hefei 230009, China;
(4) Zhongxiang Liu:
School of Transportation, Southeast University, Nanjing 210096, China;
(5) Zhongxiang Liu, Zhen Sun:
State Key Laboratory of Safety, Durability and Healthy Operation of Long Span Bridges, Nanjing 211189, China.
Abstract
This study integrates performance analysis, structural control, and SHM for a high-rise building. For this study, a Tower Structure equipped with a Tuned Mass Damper (TMD) control system and in the process of having its SHM system installed was selected. In this study, the abbreviation "BT" is used for the examined tower structure. For the SHM system, which will continuously monitor the dynamic behavior of the structure through accelerometers and sensors placed on critical floors in BT, additional threshold values based on performance analyses carried out using numerical dynamic analysis, were proposed. Critical parameters such as floor displacements, vibration characteristics, and peak displacements obtained from dynamic analyses can be used as additional threshold values that can be monitored by the SHM system. Thus, these data can be incorporated into operational decision criteria that can be transferred to the building control system. This situation could enable the evaluation of analysis results beyond design and verification use. For these purposes, the building equipped with TMD was subjected to time-historical nonlinear dynamic analyses using the finite element method (FEM). Response values under unconfined and forced vibrations were obtained using two earthquake records with different characteristics (Kobe and Chi Chi earthquakes). It has been suggested that the story drift ratio and top displacement values obtained from wind analysis also be used as additional upper threshold values to define the "normal" operating range of the TMD. It is claimed that using all these additional thresholds together with the main thresholds will significantly reduce false alarm rates.
Key Words
earthquake engineering; finite element modeling; nonlinear structural behavior; seismic response; Structural Health Monitoring (SHM); tuned mass dampers; uncertainty quantification; wind-induced response
Address
(1) Azer Kasımzade, Emin Nematlı:
Azerbaijan University of Architecture and Construction, Baku, Azerbaijan;
(2) Varol Koç:
Ondokuz Mayis University, Samsun, Turkey.
Abstract
For vision-guided automatic assembly construction robots, binocular camera calibration is essential for accurate three-dimensional perception and spatial localization. However, large field-of-view (FOV) camera systems often suffer from degraded calibration accuracy due to low-resolution and blurred checkerboard features captured at long working distances. Although conventional single-image super-resolution (SISR) methods improve image quality, they may introduce structural distortions that compromise geometric fidelity and reduce calibration accuracy. To address this issue, a Geometrically Constrained Super-Resolution Reconstruction Network (GC-SRNet) is proposed for geometry-preserving image reconstruction in camera calibration. The proposed framework integrates geometric priors with dual-level geometric supervision strategy. Specifically, the Geometric Feature Awareness Module (GFAM) and Geometric Constraint Refinement Module (GCRM) are developed to extract and refine geometry-sensitive features, while the supervision mechanism preserves geometric consistency during reconstruction. In addition, a calibration-oriented evaluation metric, termed TSR, is introduced to quantify the recovery degree of calibration performance from super-resolved images toward high-resolution references. Experimental results demonstrate that, at a ×4 scaling factor, GC-SRNet achieves a PSNR of 35.68 dB, an SSIM of 91.55%, and a TSR of 99.15%. Field experiments further show that the calibration reprojection error is reduced from 3.2711 pixels to 0.2139 pixels for a binocular camera system with a resolution of 1280 × 960 pixels. The results indicate that GC-SRNet effectively preserves geometric fidelity while improving image quality, providing a practical solution for high-precision binocular camera calibration in vision-guided intelligent construction applications.
Key Words
binocular vision; camera calibration; construction robotics; generative adversarial networks; geometric constraints; image resolution
Address
(1) Lizhi Long, Lu Deng, Shuo Wang:
College of Civil Engineering, Hunan University, Changsha, China;
(2) Weiqi Mao:
China Railway Major Bridge Engineering Group CO., LTD, China;
(3) Lu Deng:
Key Laboratory of Damage Diagnosis for Engineering Structures of Hunan Province, Hunan University, Changsha, China;
(4) Lu Deng:
State Key Laboratory of Bridge Safety and Resilience, Hunan University, Changsha, China;
(5) Yuanfeng Duan:
College of Civil Engineering and Architecture, Guangxi University, Nanning, China.
