Topic Editors

School of Highway, Chang’an University, Xi’an 710064, China
Dr. Jingfeng Zhang
School of Highway, Chang’an University, Xi’an 710064, China
School of Civil Engineering, University College Dublin, D04V1W8 Dublin, Ireland
Department of Bridge Engineering, Southwest Jiaotong University, Chengdu 610031, China
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

AI Enhanced Civil Infrastructure Safety

Abstract submission deadline
closed (30 October 2023)
Manuscript submission deadline
closed (30 December 2023)
Viewed by
32841

Topic Information

Dear Colleagues,

Due to the critical role of civil infrastructure in modern society, it should be able to remain safe and reliable under service environments or accident disasters, such as earthquakes, rockfalls, tsunamis, fires, blasts, etc. Maintaining the safety of civil infrastructure was, is and will continue to be a significant research topic. Although magnificent progress has been made, there are still critical challenges related to the demand for more accurate, efficient and pragmatic safety assessment and analysis of civil infrastructures under multiple scenes due to the intrinsic failure mechanism of materials and the large uncertainty within external effects. With more high-performance materials being introduced, this challenge becomes trickier. However, with the rapid development of the AI field, a brand-new opportunity has emerged to reveal these mechanisms and uncertainties and tackle the above challenge with AI's assistance. From this perspective, this topic aims to invite relevant scholars and collect the innovative outcomes of their research in civil and infrastructural safety via AI-enhanced, multi-disciplinary principles. We hope this Topic will be a platform for sharing novel knowledge and stimulating new ideas. The specific topics include, but are not limited to, new developments in the following:

  • Data-driven material and component performance prediction
  • AI-enhanced structural behavior analysis
  • Structural design upgraded by AI
  • AI-aided structure construction techniques
  • Structure maintenance with smart sensing
  • Structural damage inspection based on AI
  • AI applications in structural health monitoring
  • Smart structural maintenance management
  • AI-aided optimization of conformation and structure.

Dr. Shizhi Chen
Dr. Jingfeng Zhang
Dr. Ekin Ozer
Dr. Zilong Ti
Dr. Xiaoming Lei
Topic Editors

Keywords

  • analysis under multiple hazards
  • design and construction
  • assessment and enhancement
  • structural inspection
  • structural performance prediction
  • maintenance optimization
  • machine learning
  • deep learning
  • heuristic optimization algorithm
  • smart sensing technology

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400
Buildings
buildings
3.8 3.1 2011 14.6 Days CHF 2600
Infrastructures
infrastructures
2.6 4.3 2016 16.9 Days CHF 1800
Materials
materials
3.4 5.2 2008 13.9 Days CHF 2600
Sensors
sensors
3.9 6.8 2001 17 Days CHF 2600
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700
Inventions
inventions
3.4 5.4 2016 17.4 Days CHF 1800

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Published Papers (28 papers)

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19 pages, 2383 KiB  
Article
Analyzing the Factors Influencing Time Delays in Korean Railroad Accidents
Appl. Sci. 2024, 14(5), 1697; https://doi.org/10.3390/app14051697 - 20 Feb 2024
Viewed by 256
Abstract
Railroads play a pivotal role in the Korean national economy, necessitating a thorough understanding of factors influencing accidents for effective mitigation strategies. Unlike prior research focused on accident frequency and severity, this study delves into the often-overlooked aspect of time delays resulting from [...] Read more.
Railroads play a pivotal role in the Korean national economy, necessitating a thorough understanding of factors influencing accidents for effective mitigation strategies. Unlike prior research focused on accident frequency and severity, this study delves into the often-overlooked aspect of time delays resulting from railroad accidents. Analyzing 15 years of nationwide data (2008–2022), encompassing 3244 human-related and 3350 technical events, this research identifies key factors influencing delay likelihood and duration. Factors considered include event type, season, train type, location, operator size, person type involved, facility type, and causes. Despite an overall decrease in events, variable delay times highlight the need to comprehend specific contributing factors. To address excess zeros, the study employs a two-stage model and a zero-inflated negative binomial (ZINB) model, alongside artificial neural networks (ANNs) for non-linear pattern recognition. Human-related delays are influenced by event types, seasons, and passenger categories, exhibit nuanced impacts. Technical-related delays are influenced by incident types and facility involvement. Regarding model performance, the ANN models outperform regression-based models consistently in all cases. This study emphasizes the importance of considering both human and technical factors in predicting and understanding railroad accident delays, offering valuable insights for formulating strategies to mitigate service disruptions associated with these incidents. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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18 pages, 5299 KiB  
Article
A Machine-Learning-Based Failure Mode Classification Model for Reinforced Concrete Columns Using Simple Structural Information
Appl. Sci. 2024, 14(3), 1243; https://doi.org/10.3390/app14031243 - 02 Feb 2024
Viewed by 353
Abstract
The seismically deficient column details in existing reinforced concrete buildings affect the overall behavior of the building depending on the failure type of the column. The purpose of this study is to develop and validate a machine-learning-based prediction model for the column failure [...] Read more.
The seismically deficient column details in existing reinforced concrete buildings affect the overall behavior of the building depending on the failure type of the column. The purpose of this study is to develop and validate a machine-learning-based prediction model for the column failure modes (shear, flexure–shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using the concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating the accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model has the highest average value for the classification model performance measurements among the considered learning methods and can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with the simple column details. Additionally, it was demonstrated that the predicted failure modes from the selected model were exactly same as the failure mode determined from a code-defined equation (traditional method). Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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18 pages, 9114 KiB  
Article
Experimental Study on Measuring and Tracking Structural Displacement Based on Surveillance Video Image Analysis
Sensors 2024, 24(2), 601; https://doi.org/10.3390/s24020601 - 17 Jan 2024
Viewed by 493
Abstract
The digital image method of monitoring structural displacement is receiving more attention today, especially in non-contact structure health monitoring. Some obvious advantages of this method, such as economy and convenience, were shown while it was used to monitor the deformation of the bridge [...] Read more.
The digital image method of monitoring structural displacement is receiving more attention today, especially in non-contact structure health monitoring. Some obvious advantages of this method, such as economy and convenience, were shown while it was used to monitor the deformation of the bridge structure during the service period. The image processing technology was used to extract structural deformation feature information from surveillance video images containing structural displacement in order to realize a new non-contact online monitoring method in this paper. The influence of different imaging distances and angles on the conversion coefficient (η) that converts the pixel coordinates to the actual displacement was first studied experimentally. Then, the measuring and tracking of bridge structural displacement based on surveillance video images was investigated by laboratory-scale experiments under idealized conditions. The results showed that the video imaging accuracy can be affected by changes in the relative position of the imaging device and measured structure, which is embodied in the change in η (actual size of individual pixel) on the structured image. The increase in distance between the measured structure and the monitoring equipment will have a significant effect on the change in the η value. The value of η varies linearly with the change in shooting distance. The value of η will be affected by the changes in shooting angle. The millimeter-level online monitoring of the structure displacement can be realized using images based on surveillance video images. The feasibility of measuring and tracking structural displacement based on surveillance video images was confirmed by a laboratory-scale experiment. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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20 pages, 2272 KiB  
Article
Modeling the Optimal Maintenance Strategy for Bridge Elements Based on Agent Sequential Decision Making
Appl. Sci. 2024, 14(1), 14; https://doi.org/10.3390/app14010014 - 19 Dec 2023
Viewed by 429
Abstract
In addressing the issues of low efficiency in bridge maintenance decision making, the inaccurate estimation of maintenance costs, and the lack of specificity in decision making regarding maintenance measures for specific defects, this study utilizes data from regular bridge inspections. It employs a [...] Read more.
In addressing the issues of low efficiency in bridge maintenance decision making, the inaccurate estimation of maintenance costs, and the lack of specificity in decision making regarding maintenance measures for specific defects, this study utilizes data from regular bridge inspections. It employs a two-parameter Weibull distribution to model the duration variables of the states of bridge elements, thereby enabling the prediction of the duration time of bridge elements in various states. Referring to existing bridge maintenance and repair regulations, the estimation process of maintenance costs is streamlined. Taking into account the specific types and development state of bridge defects, as well as considering the adequacy of maintenance and the restorative effects of maintenance measures, an intelligent agent sequential decision-making model for bridge maintenance decisions is established. The model utilizes dynamic programming algorithms to determine the optimal maintenance and repair measures for elements in various states. The decision results are precise, all the way down to the specific bridge elements and maintenance measures for individual defects. In using the case of the regular inspection data of 222 bridges along a highway loop, this study further validates the effectiveness of the proposed research methods. By constructing an intelligent agent sequential decision-making model for bridge element maintenance, the optimal maintenance measures for 21 bridge elements in different states are obtained, thereby significantly enhancing the efficiency of actual bridge maintenance and the practicality of decision results. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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16 pages, 4270 KiB  
Article
A Practical Data Extraction, Cleaning, and Integration Method for Structural Condition Assessment of Highway Bridges
Infrastructures 2023, 8(12), 183; https://doi.org/10.3390/infrastructures8120183 - 18 Dec 2023
Viewed by 1314
Abstract
The success of regional bridge condition assessment, a crucial component of systematic maintenance strategies, relies heavily on comprehensive, well-structured regional bridge databases. This study proposes the data extraction, cleaning, and integration method for the construction of such databases. First, this research proposes an [...] Read more.
The success of regional bridge condition assessment, a crucial component of systematic maintenance strategies, relies heavily on comprehensive, well-structured regional bridge databases. This study proposes the data extraction, cleaning, and integration method for the construction of such databases. First, this research proposes an extraction method tailored for unstructured data often present in inspection reports. Additionally, this paper meticulously outlines a cleaning procedure designed to rectify two distinct categories of typical errors that are present within the inspection data. Subsequently, this study takes a holistic approach by establishing integration rules that harmonize data from various sources, including inspection records, monitoring data, traffic statistics, as well as design and construction blueprints. The architectural framework of the regional bridge information database is then meticulously laid out. To validate and demonstrate the effectiveness of the method, this study applies them to a set of representative highway bridges situated within Shandong Province. The results show that this approach can be used to successfully establish a functional regional bridge database. The database plays a pivotal role in harnessing the latent potential of an extensive range of multi-source information and propels the field of bridge condition assessment forward by providing a solid basis for informed decision making and strategic planning in the realm of infrastructure maintenance. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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18 pages, 6083 KiB  
Article
Predicting Trajectories of Plate-Type Wind-Borne Debris in Turbulent Wind Flow with Uncertainties
Infrastructures 2023, 8(12), 180; https://doi.org/10.3390/infrastructures8120180 - 15 Dec 2023
Viewed by 1186
Abstract
Debris poses multifaceted risks and jeopardizes various aspects of the environment, human health, safety, and infrastructure. The debris trajectory in turbulent wind flow is more dispersed due to the inherent randomness of the turbulent winds. This paper investigates the three-dimensional trajectories of plate-type [...] Read more.
Debris poses multifaceted risks and jeopardizes various aspects of the environment, human health, safety, and infrastructure. The debris trajectory in turbulent wind flow is more dispersed due to the inherent randomness of the turbulent winds. This paper investigates the three-dimensional trajectories of plate-type wind-borne debris in turbulent wind fields via the method of numerical simulation. A 3D probabilistic trajectory model of plate-type wind-borne debris is developed. The debris trajectories are numerically calculated by solving the governing equation of debris motion and by introducing turbulent wind flows based on the near-ground wind field measured in the wind tunnel to account for the probability characteristics of the debris trajectory. The dimensionless velocities and displacements of the debris trajectory show good agreement with the experimental data in wind tunnel tests, confirming the rationality of the probabilistic trajectory model. Based on the validated trajectory model, the probability characteristics of the debris impact position, impact velocity, and kinetic energy, debris angular displacement, and angular velocity are analyzed in detail under five different wind attack angles. The proposed probabilistic model of plate-type debris in turbulent wind flow provides an accurate and effective method for predicting debris trajectory in three-dimensional space. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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18 pages, 13626 KiB  
Article
Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method
Remote Sens. 2023, 15(24), 5712; https://doi.org/10.3390/rs15245712 - 13 Dec 2023
Viewed by 550
Abstract
The health and stability of trees are essential information for the safety of people and property in urban greenery, parks or along roads. The stability of the trees is linked to root stability but essentially also to trunk decay. Currently used internal tree [...] Read more.
The health and stability of trees are essential information for the safety of people and property in urban greenery, parks or along roads. The stability of the trees is linked to root stability but essentially also to trunk decay. Currently used internal tree stem decay assessment methods, such as tomography and penetrometry, are reliable but usually time-consuming and unsuitable for large-scale surveys. Therefore, a new method based on close-range remotely sensed data, specifically close-range photogrammetry and iPhone LiDAR, was tested to detect decayed standing tree trunks automatically. The proposed study used the PointNet deep learning algorithm for 3D data classification. It was verified in three different datasets consisting of pure coniferous trees, pure deciduous trees, and mixed data to eliminate the influence of the detectable symptoms for each group and species itself. The mean achieved validation accuracies of the models were 65.5% for Coniferous trees, 58.4% for Deciduous trees and 57.7% for Mixed data classification. The accuracies indicate promising data, which can be either used by practitioners for preliminary surveys or for other researchers to acquire more input data and create more robust classification models. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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14 pages, 4772 KiB  
Article
Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
Appl. Sci. 2023, 13(24), 13204; https://doi.org/10.3390/app132413204 - 12 Dec 2023
Viewed by 546
Abstract
Currently, damage in aging bridges is assessed visually, leading to significant personnel, time, and cost expenditures. Moreover, the results depend on the subjective judgment of the inspector. Machine-learning-based approaches, such as deep learning, can solve these problems. In particular, instance-segmentation models have been [...] Read more.
Currently, damage in aging bridges is assessed visually, leading to significant personnel, time, and cost expenditures. Moreover, the results depend on the subjective judgment of the inspector. Machine-learning-based approaches, such as deep learning, can solve these problems. In particular, instance-segmentation models have been used to identify different types of bridge damage. However, the value of deep-learning-based damage identification may be reduced by insufficient training data, class imbalance, and model-reliability issues. To overcome these limitations, this study utilized photographic data from real bridge-management systems for the inspection and assessment of bridges as the training dataset. Six types of damage were considered. Moreover, the performances of three representative deep learning models—Mask R-CNN, BlendMask, and SWIN—were compared in terms of loss–function values. SWIN showed the best performance, achieving a loss value of 0.000005 after 269,939 training iterations. This shows that bridge-damage-identification performance can be maximized by setting an appropriate learning rate and using a deep learning model with a minimal loss value. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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23 pages, 9044 KiB  
Article
Seismic Performance Evaluation and Analysis of Vertical Hydrogen Storage Vessels Based on Shaking Table Testing
Appl. Sci. 2023, 13(24), 13190; https://doi.org/10.3390/app132413190 - 12 Dec 2023
Viewed by 632
Abstract
In this study, the structural integrity of a system installed on protrusion concrete, considering the usability of a vertical hydrogen storage vessel, was verified. To achieve this, a site survey was conducted to select the target structure, and analytical validation was performed to [...] Read more.
In this study, the structural integrity of a system installed on protrusion concrete, considering the usability of a vertical hydrogen storage vessel, was verified. To achieve this, a site survey was conducted to select the target structure, and analytical validation was performed to design specimens for shaking table tests. Subsequently, dynamic behavior characteristics were analyzed using an artificial earthquake simulated according to the procedures outlined in ICC-ES AC 156, which is the seismic design criterion. As a result, it was observed that the seismic motion was amplified by approximately 10 times compared to the original load magnitude, based on the acceleration response of the test specimen. It is inferred that the seismic motion occurring during an earthquake could cause significant damage to both the internal and external aspects of the structure, depending on the structure’s form and the composition of materials. Through analytical verification and testing, it was revealed that the main structure of the test specimen and the anchor bolts for installation met the seismic performance criteria. However, the protrusion concrete area exhibited damage, indicating a structural vulnerability when subjected to external forces such as earthquakes. Consequently, on-site measures to address this structural risk need to be explored. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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17 pages, 3118 KiB  
Article
Risk Assessment in the Design of Railroad Control Command and Signaling Devices Using Fuzzy Sets
Appl. Sci. 2023, 13(22), 12460; https://doi.org/10.3390/app132212460 - 17 Nov 2023
Viewed by 500
Abstract
Risk assessment in the design of control command and signaling devices (CCS) is one of the elements required by law. These analyses should be carried out at many stages of investment with the participation of various teams. This article presents a risk analysis [...] Read more.
Risk assessment in the design of control command and signaling devices (CCS) is one of the elements required by law. These analyses should be carried out at many stages of investment with the participation of various teams. This article presents a risk analysis method based on fuzzy sets, which can support and increase the safety of investment processes involving the railroad traffic control industry. The article analyzes hazards identified in CCS design. These risks were identified using a survey method based on a set of questions prepared by the authors and by conducting interviews among experts from design offices. As part of the survey, responses were obtained from 28 respondents who are specialists in the railway traffic control industry. Workshop meetings were held in six different design offices and at manufacturing plants of motion control systems. The identified risks were analyzed using the FMEA (failure mode and effect analysis) method and the fuzzy set method, as well as various methods of fuzzification and defuzzification. The results of all of the methods were compared with each other. The best solution from the analyzed ones was proposed. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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16 pages, 5701 KiB  
Article
Deep Learning Based Fire Risk Detection on Construction Sites
Sensors 2023, 23(22), 9095; https://doi.org/10.3390/s23229095 - 10 Nov 2023
Viewed by 783
Abstract
The recent large-scale fire incidents on construction sites in South Korea have highlighted the need for computer vision technology to detect fire risks before an actual occurrence of fire. This study developed a proactive fire risk detection system by detecting the coexistence of [...] Read more.
The recent large-scale fire incidents on construction sites in South Korea have highlighted the need for computer vision technology to detect fire risks before an actual occurrence of fire. This study developed a proactive fire risk detection system by detecting the coexistence of an ignition source (sparks) and a combustible material (urethane foam or Styrofoam) using object detection on images from a surveillance camera. Statistical analysis was carried out on fire incidences on construction sites in South Korea to provide insight into the cause of the large-scale fire incidents. Labeling approaches were discussed to improve the performance of the object detectors for sparks and urethane foams. Detecting ignition sources and combustible materials at a distance was discussed in order to improve the performance for long-distance objects. Two candidate deep learning models, Yolov5 and EfficientDet, were compared in their performance. It was found that Yolov5 showed slightly higher mAP performances: Yolov5 models showed mAPs from 87% to 90% and EfficientDet models showed mAPs from 82% to 87%, depending on the complexity of the model. However, Yolov5 showed distinctive advantages over EfficientDet in terms of easiness and speed of learning. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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27 pages, 13491 KiB  
Article
Safety Evaluation of Reinforced Concrete Structures Using Multi-Source Fusion Uncertainty Cloud Inference and Experimental Study
Sensors 2023, 23(20), 8638; https://doi.org/10.3390/s23208638 - 22 Oct 2023
Viewed by 685
Abstract
Structural damage detection and safety evaluations have emerged as a core driving force in structural health monitoring (SHM). Focusing on the multi-source monitoring data in sensing systems and the uncertainty caused by initial defects and monitoring errors, in this study, we develop a [...] Read more.
Structural damage detection and safety evaluations have emerged as a core driving force in structural health monitoring (SHM). Focusing on the multi-source monitoring data in sensing systems and the uncertainty caused by initial defects and monitoring errors, in this study, we develop a comprehensive method for evaluating structural safety, named multi-source fusion uncertainty cloud inference (MFUCI), that focuses on characterizing the relationship between condition indexes and structural performance in order to quantify the structural health status. Firstly, based on cloud theory, the cloud numerical characteristics of the condition index cloud drops are used to establish the qualitative rule base. Next, the proposed multi-source fusion generator yields a multi-source joint certainty degree, which is then transformed into cloud drops with certainty degree information. Lastly, a quantitative structural health evaluation is performed through precision processing. This study focuses on the numerical simulation of an RC frame at the structural level and an RC T-beam damage test at the component level, based on the stiffness degradation process. The results show that the proposed method is effective at evaluating the health of components and structures in a quantitative manner. It demonstrates reliability and robustness by incorporating uncertainty information through noise immunity and cross-domain inference, outperforming baseline models such as Bayesian neural network (BNN) in uncertainty estimations and LSTM in point estimations. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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16 pages, 1301 KiB  
Article
Probabilistic Shear Strength Prediction for Deep Beams Based on Bayesian-Optimized Data-Driven Approach
Buildings 2023, 13(10), 2471; https://doi.org/10.3390/buildings13102471 - 28 Sep 2023
Cited by 1 | Viewed by 564
Abstract
To ensure the safety of buildings, accurate and robust prediction of a reinforced concrete deep beam’s shear capacity is necessary to avoid unpredictable accidents caused by brittle failure. However, the failure mechanism of reinforced concrete deep beams is very complicated, has not been [...] Read more.
To ensure the safety of buildings, accurate and robust prediction of a reinforced concrete deep beam’s shear capacity is necessary to avoid unpredictable accidents caused by brittle failure. However, the failure mechanism of reinforced concrete deep beams is very complicated, has not been fully elucidated, and cannot be accurately described by simple equations. To solve this issue, machine learning techniques have been utilized and corresponding prediction models have been developed. Nevertheless, these models can only provide deterministic prediction results of the scalar type, and the confidence level is uncertain. Thus, these prediction results cannot be used for the design and assessment of deep beams. Therefore, in this paper, a probabilistic prediction approach of the shear strength of reinforced concrete deep beams is proposed based on the natural gradient boosting algorithm trained on a collected database. A database of 267 deep beam experiments was utilized, with 14 key parameters identified as the inputs related to the beam geometry, material properties, and reinforcement details. The proposed NGBoost model was compared to empirical formulas from design codes and other machine learning methods. The results showed that the NGBoost model achieved higher accuracy in mean shear strength prediction, with an R2 of 0.9045 and an RMSE of 38.8 kN, outperforming existing formulas by over 50%. Additionally, the NGBoost model provided probabilistic predictions of shear strength as probability density functions, enabling reliable confidence intervals. This demonstrated the capability of the data-driven NGBoost approach for robust shear strength evaluation of RC deep beams. Overall, the results illustrated that the proposed probabilistic prediction approach dramatically surpassed the current formulas adopted in design codes and machine learning models in both prediction accuracy and robustness. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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17 pages, 5765 KiB  
Article
LCA-YOLOv8-Seg: An Improved Lightweight YOLOv8-Seg for Real-Time Pixel-Level Crack Detection of Dams and Bridges
Appl. Sci. 2023, 13(19), 10583; https://doi.org/10.3390/app131910583 - 22 Sep 2023
Cited by 2 | Viewed by 2216
Abstract
Remotely operated vehicles (ROVs) and unmanned aerial vehicles (UAVs) provide a solution for dam and bridges structural health information acquisition, but problems like effective damage-related information extraction also occur. Vision-based crack detection methods can replace traditional manual inspection and achieve fast and accurate [...] Read more.
Remotely operated vehicles (ROVs) and unmanned aerial vehicles (UAVs) provide a solution for dam and bridges structural health information acquisition, but problems like effective damage-related information extraction also occur. Vision-based crack detection methods can replace traditional manual inspection and achieve fast and accurate crack detection. This paper thereby proposes a lightweight, real-time, pixel-level crack detection method based on an improved instance segmentation model. A lightweight backbone and a novel efficient prototype mask branch are proposed to decrease the complexity of the model and maintain the accuracy of the model. The proposed method attains an accuracy of 0.945 at 129 frames per second (FPS). Moreover, our model has smaller volume, lower computational requirements and is suitable for low-performance devices. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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14 pages, 5420 KiB  
Article
Probabilistic Seismic Hazard Analysis of a Back Propagation Neural Network Predicting the Peak Ground Acceleration
Appl. Sci. 2023, 13(17), 9790; https://doi.org/10.3390/app13179790 - 30 Aug 2023
Viewed by 816
Abstract
Probabilistic seismic hazard analysis (PSHA) has been recognized as a reasonable method for quantifying seismic threats. Traditionally, this method ignores the effect of the focal depth, in which the ground motion prediction equations (GMPEs) are applied to estimate the probability distribution associated with [...] Read more.
Probabilistic seismic hazard analysis (PSHA) has been recognized as a reasonable method for quantifying seismic threats. Traditionally, this method ignores the effect of the focal depth, in which the ground motion prediction equations (GMPEs) are applied to estimate the probability distribution associated with the possible motion levels induced by the site earthquakes, but it is limited by the unclear geological conditions, which makes it difficult to provide a uniform equation, and these equations cannot express the non-linear relationship under geological conditions. Hence, this paper proposed a method to consider the seismic focal depth for the PSHA with the example of California and used a back propagation neural network (BPNN) to predict the peak ground acceleration (PGA) instead of the GMPEs. Firstly, the measured PGA and unknown PGA seismic data applicable to this method were collected separately. Secondly, the unknown PGA data were supplemented by applying the BPNN based on the measured PGA data. Lastly, based on the full-probability equation, PSHA considering the focal depth was completed and compared with the current California seismic zoning results. The results showed that using the BPNN in the PSHA can ensure computational accuracy and universality, making it more suitable for regions with unclear geological structures and providing the possibility of adding other parameters to be considered for the influence of the PSHA. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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22 pages, 7303 KiB  
Article
Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO
Sensors 2023, 23(14), 6318; https://doi.org/10.3390/s23146318 - 11 Jul 2023
Cited by 3 | Viewed by 1539
Abstract
The construction industry is accident-prone, and unsafe behaviors of construction workers have been identified as a leading cause of accidents. One important countermeasure to prevent accidents is monitoring and managing those unsafe behaviors. The most popular way of detecting and identifying workers’ unsafe [...] Read more.
The construction industry is accident-prone, and unsafe behaviors of construction workers have been identified as a leading cause of accidents. One important countermeasure to prevent accidents is monitoring and managing those unsafe behaviors. The most popular way of detecting and identifying workers’ unsafe behaviors is the computer vision-based intelligent monitoring system. However, most of the existing research or products focused only on the workers’ behaviors (i.e., motions) recognition, limited studies considered the interaction between man-machine, man-material or man-environments. Those interactions are very important for judging whether the workers’ behaviors are safe or not, from the standpoint of safety management. This study aims to develop a new method of identifying construction workers’ unsafe behaviors, i.e., unsafe interaction between man-machine/material, based on ST-GCN (Spatial Temporal Graph Convolutional Networks) and YOLO (You Only Look Once), which could provide more direct and valuable information for safety management. In this study, two trained YOLO-based models were, respectively, used to detect safety signs in the workplace, and objects that interacted with workers. Then, an ST-GCN model was trained to detect and identify workers’ behaviors. Lastly, a decision algorithm was developed considering interactions between man-machine/material, based on YOLO and ST-GCN results. Results show good performance of the developed method, compared to only using ST-GCN, the accuracy was significantly improved from 51.79% to 85.71%, 61.61% to 99.11%, and 58.04% to 100.00%, respectively, in the identification of the following three kinds of behaviors, throwing (throwing hammer, throwing bottle), operating (turning on switch, putting bottle), and crossing (crossing railing and crossing obstacle). The findings of the study have some practical implications for safety management, especially workers’ behavior monitoring and management. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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18 pages, 7420 KiB  
Article
Research on a Ship Deflection Anti-Collision Method Based on a Water-Jet Interference Flow Field
Appl. Sci. 2023, 13(13), 7354; https://doi.org/10.3390/app13137354 - 21 Jun 2023
Cited by 1 | Viewed by 740
Abstract
Currently, water jets are mainly used in the fields of mechanical processing and mining collection. This paper creatively introduces them to the field of safety assurance for inland navigation. Compared with the traditional bridge anti-striking methods such as intelligent early warning and passive [...] Read more.
Currently, water jets are mainly used in the fields of mechanical processing and mining collection. This paper creatively introduces them to the field of safety assurance for inland navigation. Compared with the traditional bridge anti-striking methods such as intelligent early warning and passive anti-striking, this method can form an “interference zone” by changing the water flow conditions in the local bridge water areas, causing the yawing moment of the yaw ship to change, thereby causing the ship’s course to change, and thus guiding the ship to move away from the bridge pier to realize active anti-striking of the ship. In this paper, a combination of generalized model testing and numerical simulation was used to study the effects of different nozzle pressures and different ship pier distances of the water-jet generator on the trajectory and drift angle of the stalled ship. The results showed that the numerical simulation was in good agreement with the model test results. Within the interference zone, the distance between the ship and the pier increased rapidly after the action of the disturbance zone to 9.1, 5.8, and 6.2 times the ship’s width, respectively, reaching a safe distance. During the process of being affected by the interference zone, the maximum drift angle of the yaw ship was less than 20°, the course of the ship was generally stable, and the drift angle comparison error was a maximum of 10.6%, a minimum of 3.5%, and an average error of 6.7%. A negative peak and a positive peak of four times the absolute value of the negative peak occurred in the yaw-moment ephemeral curve during the ship’s passage through the interference area. The method had a notable effect on the anti-striking of stalled ships and two invention patents applied for in the course of research. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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30 pages, 7933 KiB  
Article
Distributed Compressive Sensing for Wireless Signal Transmission in Structural Health Monitoring: An Adaptive Hierarchical Bayesian Model-Based Approach
Sensors 2023, 23(12), 5661; https://doi.org/10.3390/s23125661 - 17 Jun 2023
Cited by 1 | Viewed by 866
Abstract
Signal transmission plays an important role in the daily operation of structural health monitoring (SHM) systems. In wireless sensor networks, transmission loss often occurs and threatens reliable data delivery. The massive amount of data monitoring also leads to a high signal transmission and [...] Read more.
Signal transmission plays an important role in the daily operation of structural health monitoring (SHM) systems. In wireless sensor networks, transmission loss often occurs and threatens reliable data delivery. The massive amount of data monitoring also leads to a high signal transmission and storage cost throughout the system’s service life. Compressive Sensing (CS) provides a novel perspective on alleviating these problems. Based on the sparsity of vibration signals in the frequency domain, CS can reconstruct a nearly complete signal from just a few measurements. This can improve the robustness of data loss while facilitating data compression to reduce transmission demands. Extended from CS methods, distributed compressive sensing (DCS) can exploit the correlation across multiple measurement vectors (MMV) to jointly recover the multi-channel signals with similar sparse patterns, which can effectively enhance the reconstruction quality. In this paper, a comprehensive DCS framework for wireless signal transmission in SHM is constructed, incorporating the process of data compression and transmission loss together. Unlike the basic DCS formulation, the proposed framework not only activates the inter-correlation among channels but also provides flexibility and independence to single-channel transmission. To promote signal sparsity, a hierarchical Bayesian model using Laplace priors is built and further improved as the fast iterative DCS-Laplace algorithm for large-scale reconstruction tasks. Vibration signals (e.g., dynamic displacement and accelerations) acquired from real-life SHM systems are used to simulate the whole process of wireless transmission and test the algorithm’s performance. The results demonstrate that (1) DCS-Laplace is an adaptative algorithm that can actively adapt to signals with various sparsity by adjusting the penalty term to achieve optimal performance; (2) compared with CS methods, DCS methods can effectively improve the reconstruction quality of multi-channel signals; (3) the Laplace method has advantages over the OMP method in terms of reconstruction performance and applicability, which is a better choice in SHM wireless signal transmission. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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13 pages, 3012 KiB  
Article
Void Detection inside Duct of Prestressed Concrete Bridges Based on Deep Support Vector Data Description
Appl. Sci. 2023, 13(10), 5981; https://doi.org/10.3390/app13105981 - 12 May 2023
Viewed by 833
Abstract
The tendon that is inserted into the duct is a crucial component of prestressed concrete (PSC) bridges and, when exposed to air, can quickly corrode, and cause structural collapse. It can interpret the signal measured by non-destructive testing (NDT) to determine the condition [...] Read more.
The tendon that is inserted into the duct is a crucial component of prestressed concrete (PSC) bridges and, when exposed to air, can quickly corrode, and cause structural collapse. It can interpret the signal measured by non-destructive testing (NDT) to determine the condition (normal or void) inside the duct. However, it requires the use of expensive NDT equipment such as ultrasonic waves or the hiring of experts. In this paper, we proposed an impact–echo (IE) method based on deep support vector data description (Deep SVDD) for economical void detection inside a duct. Because the pattern of IE changes for various reasons such as difference of specimen or bridge, supervised learning is not suitable. Deep SVDD is classified as normal and defective, which is a broad distribution as a hypersphere that encloses a multi-dimensional feature space for normal data represented by an autoencoder. Here, an autoencoder was developed based on the ELMo (embeddings from language model)-like structure to obtain an effective representation for IE. In the experiment, we evaluated the performance of the IE data measured in different specimens. Thus, our proposed model showed an accuracy of about 77.84% which is an improvement of up to about 47% compared to the supervised learning approach. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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19 pages, 1991 KiB  
Article
Improved YOLOv5-Based Lightweight Object Detection Algorithm for People with Visual Impairment to Detect Buses
Appl. Sci. 2023, 13(9), 5802; https://doi.org/10.3390/app13095802 - 08 May 2023
Cited by 7 | Viewed by 4754
Abstract
Object detection is crucial for individuals with visual impairment, especially when waiting for a bus. In this study, we propose a lightweight and highly accurate bus detection model based on an improved version of the YOLOv5 model. We propose integrating the GhostConv and [...] Read more.
Object detection is crucial for individuals with visual impairment, especially when waiting for a bus. In this study, we propose a lightweight and highly accurate bus detection model based on an improved version of the YOLOv5 model. We propose integrating the GhostConv and C3Ghost Modules into the YOLOv5 network to reduce the number of parameters and floating-point operations per second (FLOPs), ensuring detection accuracy while reducing the model parameters. Following that, we added the SimSPPF module to replace the SPPF in the YOLOv5 backbone for increased computational efficiency and accurate object detection capabilities. Finally, we developed a Slim scale detection model by modifying the original YOLOv5 structure in order to make the model more efficient and faster, which is critical for real-time object detection applications. According to the experimental results, the Improved-YOLOv5 outperforms the original YOLOv5 in terms of the precision, recall, and mAP@0.5. Further analysis of the model complexity reveals that the Improved-YOLOv5 is more efficient due to fewer FLOPS, with fewer parameters, less memory usage, and faster inference time capabilities. The proposed model is smaller and more feasible to implement in resource-constrained mobile devices and a promising option for bus detection systems. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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28 pages, 10901 KiB  
Article
Three-Dimensional Temperature Field Simulation and Analysis of a Concrete Bridge Tower Considering the Influence of Sunshine Shadow
Appl. Sci. 2023, 13(8), 4769; https://doi.org/10.3390/app13084769 - 10 Apr 2023
Viewed by 1115
Abstract
This paper forms a set of three-dimensional temperature field simulation methods considering the influence of sunshine shadow based on the DFLUX subroutine and FILM subroutine interface provided by the Abaqus platform to simulate the three-dimensional temperature field of concrete bridge towers and study [...] Read more.
This paper forms a set of three-dimensional temperature field simulation methods considering the influence of sunshine shadow based on the DFLUX subroutine and FILM subroutine interface provided by the Abaqus platform to simulate the three-dimensional temperature field of concrete bridge towers and study its distribution law. The results show that the method has high accuracy for shadow recognition and temperature field calculation. The maximum difference between the shadow recognition results and the theoretical calculation value was only 19.1 mm, and the maximum difference between the simulated temperature and the measured temperature was 3.3 °C. The results of analyzing the temperature field of the concrete bridge tower using this algorithm show that the temperature difference between the opposite external surface of the tower column can reach 11.6 °C, which is significantly greater than the recommended temperature difference value of 5 °C in the specifications. For the concrete bridge tower, in the thickness direction of the tower wall, the temperature change was obvious only at a range of 0.3 m from the external surface of the tower wall, and the temperature change in the remaining range was small. In addition, the temperature gradient distribution of the sunshine temperature field in the direction of wall thickness conformed to the exponential function T(x) = T0eαx + C. Additionally, the data fitting results indicate that using the temperature data at a distance of 0.8 m from the external surface as the calculation parameter in the function can achieve the ideal fitting result. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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17 pages, 3908 KiB  
Article
Measuring the Distance between Trees and Power Lines under Wind Loads to Assess the Heightened Potential Risk of Wildfire
Remote Sens. 2023, 15(6), 1485; https://doi.org/10.3390/rs15061485 - 07 Mar 2023
Viewed by 1865
Abstract
The incidence of wildfires caused by tree contact with high-voltage power lines has become an increasingly pressing issue in the United States. To prevent such incidents, local safety councils have established minimum clearance regulations between trees and power lines. While most studies have [...] Read more.
The incidence of wildfires caused by tree contact with high-voltage power lines has become an increasingly pressing issue in the United States. To prevent such incidents, local safety councils have established minimum clearance regulations between trees and power lines. While most studies have focused on the tree encroachment around power lines during normal weather conditions, recent catastrophic fires have been caused by strong winds. To address this gap in knowledge, we investigated the critical wind speed that heightens the risk of wildfires by calculating the distance between trees and wires. To conduct this study, we used airborne LiDAR data collected from Sonoma County in northern California and analyzed the behavior of a sample tree having a height of 19.2 m under wind loads. Our analysis showed that the main factor determining tree deflection is the ratio of the tree height to the trunk diameter. We also found that, although the probability of fire ignition is typically low under normal conditions, it is likely to increase at a wind speed of approximately 40.3 m/s. In conclusion, this research demonstrates the utility of point cloud data in identifying potentially dangerous trees and reducing the risk of fires. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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20 pages, 5296 KiB  
Article
AMSCN: A Novel Dual-Task Model for Automatic Modulation Classification and Specific Emitter Identification
Sensors 2023, 23(5), 2476; https://doi.org/10.3390/s23052476 - 23 Feb 2023
Cited by 1 | Viewed by 1323
Abstract
Specific emitter identification (SEI) and automatic modulation classification (AMC) are generally two separate tasks in the field of radio monitoring. Both tasks have similarities in terms of their application scenarios, signal modeling, feature engineering, and classifier design. It is feasible and promising to [...] Read more.
Specific emitter identification (SEI) and automatic modulation classification (AMC) are generally two separate tasks in the field of radio monitoring. Both tasks have similarities in terms of their application scenarios, signal modeling, feature engineering, and classifier design. It is feasible and promising to integrate these two tasks, with the benefit of reducing the overall computational complexity and improving the classification accuracy of each task. In this paper, we propose a dual-task neural network named AMSCN that simultaneously classifies the modulation and the transmitter of the received signal. In the AMSCN, we first use a combination of DenseNet and Transformer as the backbone network to extract the distinguishable features; then, we design a mask-based dual-head classifier (MDHC) to reinforce the joint learning of the two tasks. To train the AMSCN, a multitask cross-entropy loss is proposed, which is the sum of the cross-entropy loss of the AMC and the cross-entropy loss of the SEI. Experimental results show that our method achieves performance gains for the SEI task with the aid of additional information from the AMC task. Compared with the traditional single-task model, our classification accuracy of the AMC is generally consistent with the state-of-the-art performance, while the classification accuracy of the SEI is improved from 52.2% to 54.7%, which demonstrates the effectiveness of the AMSCN. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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16 pages, 15011 KiB  
Article
Research on Tower Mechanical Fault Classification Method Based on Multiclass Central Segmentation Hyperplane Support Vector Machine Improvement Algorithm
Appl. Sci. 2023, 13(3), 1331; https://doi.org/10.3390/app13031331 - 19 Jan 2023
Cited by 1 | Viewed by 870
Abstract
In this paper, a classification recognition algorithm for tower mechanical faults is proposed, and a multiclass central segmentation hyperplane support vector machine (CSH-SVM) is proposed to improve the existing multiclass support vector machine for problems in which a certain sample satisfies multiple hyperplanes [...] Read more.
In this paper, a classification recognition algorithm for tower mechanical faults is proposed, and a multiclass central segmentation hyperplane support vector machine (CSH-SVM) is proposed to improve the existing multiclass support vector machine for problems in which a certain sample satisfies multiple hyperplanes at the same time. The tilt angle change and wind direction data were extracted using the tilt sensors and anemometers attached to the tower, and the temperature and humidity sensors, as well as real-time rainfall and water accumulation information, were combined to construct a sample of the original dataset during the operation of the tower. The unbalanced samples were improved using the synthetic minority oversampling technique (SMOTE) algorithm to construct a balanced dataset suitable for machine learning and improve the prediction accuracy of machine learning. At the same time, the support vector machine hyperplane under the one-vs-all classification principle was additionally computed, and the new hyperplane was computed via the existing hyperplane not only to solve the classification problem of the transition area under the one-vs-all classification so that the samples located in this area no longer meet two hyperplane equations at the same time, but also to reduce the probability of incorrect classification to a certain extent. Through verification, CSH-SVM can classify 15 out of 77 misclassified samples into the correct category with slightly higher computational power than the traditional one-vs-all classification SVM, which can improve the classification prediction accuracy for unbalanced tower mechanical failure datasets and make an accurate judgment on the current state of the tower through the tower data as to when the tower may generate mechanical failure, thus reducing economic loss and personal safety threats. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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18 pages, 19254 KiB  
Article
Multiple Defects Inspection of Dam Spillway Surface Using Deep Learning and 3D Reconstruction Techniques
Buildings 2023, 13(2), 285; https://doi.org/10.3390/buildings13020285 - 18 Jan 2023
Cited by 1 | Viewed by 1367
Abstract
After a lengthy period of scouring, the reinforced concrete surface of the dam spillway (i.e., drift spillways and flood discharge spillways) will suffer from deterioration and damage. Regular manual inspection is time-consuming and dangerous. This paper presents a robotic solution to detect automatically, [...] Read more.
After a lengthy period of scouring, the reinforced concrete surface of the dam spillway (i.e., drift spillways and flood discharge spillways) will suffer from deterioration and damage. Regular manual inspection is time-consuming and dangerous. This paper presents a robotic solution to detect automatically, count defect instance numbers, and reconstruct the surface of dam spillways by incorporating the deep learning method with a visual 3D reconstruction method. The lack of a real dam defect dataset and incomplete registration of minor defects on the 3D mesh model in fusion step are two challenges addressed in the paper. We created a multi-class semantic segmentation dataset of 1711 images (with resolutions of 848 × 480 and 1280 × 720 pixels) acquired by a wall-climbing robot, including cracks, erosion, spots, patched areas, and power safety cable. Then, the architecture of the U-net is modified with pixel-adaptive convolution (PAC) and conditional random field (CRF) to segment different scales of defects, trained, validated, and tested using this dataset. The reconstruction and recovery of minor defect instances in the flow surface and sidewall are facilitated using a keyframe back-projection method. By generating an instance adjacency matrix within the class, the intersection over union (IoU) of 3D voxels is calculated to fuse multiple instances. Our segmentation model achieves an average IoU of 60% for five defect class. For the surface model’s semantic recovery and instance statistics, our method achieves accurate statistics of patched area and erosion instances in an environment of 200 m2, and the average absolute error of the number of spots and cracks has reduced from the original 13.5 to 3.5. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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11 pages, 1988 KiB  
Article
Investigation into Recognition Technology of Helmet Wearing Based on HBSYOLOX-s
Appl. Sci. 2022, 12(24), 12997; https://doi.org/10.3390/app122412997 - 18 Dec 2022
Cited by 1 | Viewed by 967
Abstract
This work proposes a new approach based on YOLOX model enhancement for the helmet-wearing real-time detection task, which is plagued by low detection accuracy, incorrect detection, and missing detection. First, in the backbone network, recursive gated convolution (gnConv) is utilized instead [...] Read more.
This work proposes a new approach based on YOLOX model enhancement for the helmet-wearing real-time detection task, which is plagued by low detection accuracy, incorrect detection, and missing detection. First, in the backbone network, recursive gated convolution (gnConv) is utilized instead of traditional convolution, hence addressing the issue of extracting many worthless features due to excessive redundancy in the process of feature extraction using conventional convolution. Replace the original FPN layer in the Neck network with the EfficientNet-BiFPN layer. Realize top–down and bottom–up bidirectional fusion of deep and shallow features to improve the flow of feature data between network layers. Lastly, the SIOU cross-entropy loss function is implemented to address the issue of missed detections in crowded environments and further increase the model’s detection precision. Experiments and data comparisons indicate that the modified model’s average detection accuracy is 95.5%, which is 5.4% higher than that of the original network model, and that the detection speed has been dramatically increased to suit actual production requirements. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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15 pages, 8225 KiB  
Article
Crack Segmentation on Earthen Heritage Site Surfaces
Appl. Sci. 2022, 12(24), 12830; https://doi.org/10.3390/app122412830 - 14 Dec 2022
Viewed by 1228
Abstract
Earthen heritage sites are historical relics left by ancient human activity, with earthen as the primary building material, and have significant historical, scientific, and artistic value. However, many sites have experienced extensive deterioration caused by environmental forces and human factors. A crack is [...] Read more.
Earthen heritage sites are historical relics left by ancient human activity, with earthen as the primary building material, and have significant historical, scientific, and artistic value. However, many sites have experienced extensive deterioration caused by environmental forces and human factors. A crack is a kind of typical damage to the walls of earthen heritage sites. Studies of the crack-formation process can effectively predict trends in damage, which will play a critical role in the maintenance of earthen heritage sites. This study is the first of its kind to propose a deep learning method to study the cracks on earthen heritage sites at the pixel-level, adopt the idea of transfer learning, and employ a mixed-crack image dataset for training three deep learning models. The precision, recall, IoU, and F1 metrics were used to evaluate the performance of the trained models. The experimental results showed that FPN-vgg16 appeared to have the highest level of applicability to detect cracks on earthen heritage sites among all networks, due to the highest F1 score of 84.40% and the highest IoU score of 73.11%. The results illustrated that the proposed method in this paper can effectively be used to analyze the rammed earth surface crack images, with great potential in related research fields. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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15 pages, 3701 KiB  
Article
Long Short-Term Memory-Based Methodology for Predicting Carbonation Models of Reinforced Concrete Slab Bridges: Case Study in South Korea
Appl. Sci. 2022, 12(23), 12470; https://doi.org/10.3390/app122312470 - 06 Dec 2022
Cited by 1 | Viewed by 1537
Abstract
Reinforced concrete slab (RCS) bridges deteriorate because of exposure to environmental factors over time, resulting in reduced durability. Particularly, the carbonation of RCS bridges corrodes the rebars and reduces the strength. However, carbonation models derived from short-term experiments exhibit low reliability with respect [...] Read more.
Reinforced concrete slab (RCS) bridges deteriorate because of exposure to environmental factors over time, resulting in reduced durability. Particularly, the carbonation of RCS bridges corrodes the rebars and reduces the strength. However, carbonation models derived from short-term experiments exhibit low reliability with respect to existing bridges. Therefore, a long short-term memory (LSTM)-based methodology was developed in this study for generating carbonation models using existing bridge inspection reports. The proposed methodology trains the LSTM model by combining data extracted from reports and local environmental data. The learning process uses padding and masking methods to consider the history of environmental data. A case study was performed to validate the proposed method in three different regions of Korea. The results verified that the coefficient of determination of the proposed method was higher than those of the existing carbonation models and other regression analyses. Therefore, the developed methodology can be used for predicting regional carbonation models using the data from existing bridges. Full article
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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