Smart geogrids embedded with fiber Bragg grating (FBG) for reinforcement as well as measurement of geotechnical structures have been developed. After the fabricating process of the geogrids is detailed, finite element (FE) simulations are conducted to analyze the strain distribution of geogrids and the strain transfer characteristics from geogrids to fiber optic. Results indicate that FBG should be deployed in the middle of the geogrids rib to make sure that uniform strain distribution along the FBG. Also, PVC protective sleeves, which are used to protect fiber optic when integrated with geogrids, have smaller strain transfer loss than nylon sleeves. Tensile experiments are conducted to test strain measurement performance of proposed geogrids, and the results demonstrate that proposed smart geogrids have good linearity and consistency. Temperature experiments show that FBG embedded in geogrids has higher temperature sensitivity, and the temperature induced error can be compensated by an extra FBG strain-independent sensor. Furthermore, designed smart geogrids are used in a geotechnical model test to monitor strain during tunnel excavation. The strain tendency measured by smart geogrids and traditional strain sensor agree very well. The results indicate that smart geogrids embedded with FBGs can be an effective method to measure strains for geological engineering related applications.

Microgrids equipped with hybrid energy storage systems (ESSs) are increasingly critical for balancing the intermittency of renewable energy sources (RESs) and the fluctuations in demand. This paper introduces a novel Multi-Agent Imitation Learning (MAIL) framework for real-time energy management in microgrids, particularly under real-time pricing conditions. The approach leverages a problem decomposition strategy, which separates the energy management task into two phases: first, optimal actions for each ESS are estimated using individual agents, each employing a deep neural network (DNN) trained to emulate an ideal mixed-integer linear programming (MILP) solver; then, a one-step online optimization reacts to these decisions to optimize the remaining system components holistically. Rigorous proofs establish the equivalence of the decomposed subproblems to the original optimization problem, ensuring integrity and effectiveness. Comparative simulation studies with real-world data reveal that our MAIL framework offers significant cost advantages and superior training efficiency relative to both traditional single and emerging multi-agent reinforcement learning (RL) methods. Notably, the operational costs achieved through our approach are markedly lower than those of its counterparts, closely approximating the theoretical minimum. This underscores the method’s proficiency and its potential as an effective solution for microgrid energy management. Moreover, the MAIL strategy’s multi-agent design allows for straightforward scalability to more extensive systems involving multiple ESSs and shows promising potential for cooperative management across interconnected microgrids.

Anomaly detection of internal defects in underground engineering structures is critical. This paper proposes an unsupervised deep learning image-to-image translation method tailored for ground penetrating radar (GPR) images. The proposed model can translate real-world GPR images to simulated ones. In this manner, labeling real GPR images is not necessary, and only the target detection model trained on simulated GPR images is required to directly identify defects in real GPR images. The unsupervised deep learning network introduces geometry-consistency constraints into the CycleGAN, which largely prevents the problem of semantic distortion in translation. Validation of the proposed method was performed using GPR data collected in various scenarios using GPR of different center frequencies and manufacturers. Moreover, to verify its adaptability and feasibility for defect recognition, commonly used deep learning-based defect recognition methods, which were trained only on simulated GPR images, were used to detect the translated GPR images. The findings indicate that the type and location of internal defects in translated GPR images can be accurately identified using the proposed method.

Periodic inspections and recognition of temporal evolution for concrete internal defects are important for the long-term operation of civil engineering infrastructures. This study proposes a method integrating convolutional neural network (CNN) with maximum intensity projection (MIP) for 3D imaging and temporal evolution recognition of concrete internal defects. The proposed method utilizes MIP to process multiple ground penetrating radar (GPR) B-scans and generates 2D projected GPR B-scans containing spatial information. SegNet coupled with the Lovász softmax loss function is introduced to reconstruct 2D defects from projected GPR B-scans. Subsequently, 3D imaging result is reconstructed from 2D imaging results by 3D reconstruction module. Finally, the defect change extraction module realizes recognition of defect changes based on 3D imaging results at different time. The superiority of proposed method is validated based on both synthetic and real GPR data, which presents better recognition results and less time consumption than existing 3D CNN-based method.

This study investigates economical scheduling of charging for an electric vehicle (EV) in a typical household with an intelligent charging management system. The problem formulation considers rooftop solar power generation, time-varying domestic energy consumption, real-time pricing of electricity, and user preferences. This task traditionally takes the form of a mixed-integer linear programming (MILP) problem, but we demonstrate its equivalence to linear programming (LP) to reduce computational complexity. The LP problem can be solved to global optimality if all future information is known, which is unrealistic in practice and replaced with forecasting. Learning-based methods such as deep reinforcement learning (DRL) eliminate the need for a forecaster and make online decisions rapidly using a learned policy. We propose an approach based on imitation learning that leverages the knowledge of an LP expert by learning from its optimal demonstrations instead of learning from scratch in DRL. Our approach trains a deep neural network (DNN) based policy efficiently in a supervised manner and incorporates a safety post-processing mechanism that enforces strict constraint satisfaction. Numerical studies on real-world data show that the proposed approach achieves 23 $ \sim $ 220 times speedup compared to DRL for DNN training, and the total electricity cost is far lower than DRL as well, which is strikingly close to the lower bound in theory. Our implementation code can be found at https://github.com/ZhenhaoH/IL_EVCS.