Journal of Performance of Constructed Facilities

Abstract

The detection and evaluation of internal corrosion of concrete bridge decks is of critical importance for bridge structure safety. Ground penetrating radar (GPR) is a nondestructive testing technique that has been used widely for corrosion assessment of RC bridge decks. However, the recognition accuracy of traditional automatic data processing methods for blurred hyperbolic features is insufficient, which leads to a decrease in the accuracy of bridge deck corrosion assessment. To address this problem, this paper proposes an automatic rebar picking algorithm based on gradient information and a migration method for bridge deck corrosion assessment with GPR data. This method can be divided into three steps: thresholding processing based on B-scan gradient information, a limited hyperbolic summation-based recognition (LHSR) algorithm, and rebar localization. First, the GPR B-scan is transformed into its gradient image and thresholded to enhance hyperbolic features. The LHSR algorithm then is applied to the binarized gradient B-scan to identify the hyperbolas, locate the rebar, and extract the rebar reflection amplitude. Finally, the corrosion map of the bridge deck is generated based on the rebar position and the rebar reflection amplitude after depth-correction. A case study with GPR data from two tested bridges was employed to validate the feasibility of the proposed method. The results show that the precision and recall of automatic rebar picking by this method for poor-quality GPR data were 91.27% and 93.56%, which are significantly higher than those of the traditional methods. Moreover, the accuracy of the bridge deck corrosion map obtained by the proposed method also is significantly better than that of the traditional methods. It can be concluded that the proposed method can be used for rebar picking and corrosion assessment of RC bridge decks with GPR data.

Practical Applications

Ground penetrating radar is a nondestructive testing technology that has been used widely for corrosion assessment of concrete bridge decks. However, the traditional automatic processing method of GPR data has insufficient recognition accuracy for blurred features of rebars, resulting in a decrease in the accuracy of bridge deck corrosion assessment. To address this problem, this paper proposes an automatic method that can be used for feature identification and location of rebars. The corrosion map generated by the position and reflection amplitude of rebars could help engineers to conduct corrosion assessment and repair of concrete bridge decks. The feasibility of the proposed method was validated through a case study with GPR data form two actual bridges. Especially for poor-quality GPR data, the accuracy of rebar picking and corrosion maps of this method is significantly higher than that of traditional methods. It can be concluded that the proposed method can be used for rebar positioning and corrosion assessment of concrete bridge decks based on GPR inspection data.

A finite element model of steel box girder with angle steel and round pipe braced trusses, which matched the construction details of the Sutong Yangtze River Bridge, was created to investigate the causes of cracks in braced trusses of longitudinal diaphragms. The stress characteristics and deformation modes of fatigue crack details on the braced trusses were studied in conjunction with nominal stress test results obtained from an actual bridge. Additionally, two retrofit interventions were proposed: replacing the braced trusses with channel steel and adding vertical braces. Based on the traffic flow data from an actual bridge, the fatigue life of four braced truss forms was predicted and compared. Finally, an optimized retrofit intervention was recommended. The results indicate that the inner side joint details between the braced trusses and upper gusset plates are particularly susceptible to fatigue cracking. Under cyclic loads from traffic, axial tensile and compressive stresses are the main contributors to fatigue. Local stress concentration, combined with initial welding defects of the braced trusses, plays a crucial role in the initiation of fatigue cracks. The use of channel steel sections can enhance the fatigue performance of joint details, while the impact of adding vertical braces on fatigue performance is minimal.

Concerning preventive maintenance plans for heritage timber buildings, computational methods are pioneering knowledge for the implementation of new preservation approaches in heritage structure management. In this context, fuzzy logic and random forest methodologies manage both data obtained from professional experts and data obtained in situ from the buildings themselves. This kind of digital procedure can harmonize the outcomes of building assessments because slight variations in the evaluation of input parameters do not produce a significant dispersion over the model’s output. Preventive conservation strategies require cooperation among qualified experts who examine multidisciplinary knowledge related to heritage properties. Thus, new digital protocols and procedures that help decision makers prioritize preventive interventions and avoid corrective actions are paramount in minimizing the irreparable loss of properties. The main aim of this research is a new approach to two computational management systems: fuzzy logic and random forest. The outcomes of this study will be useful to stakeholders who are responsible for the maintenance of heritage buildings, as this methodology reduces the probability of failure and uncertainty during decision-making. The instruments derived will establish mitigation strategies oriented toward proactive future maintenance programs for heritage timber buildings in southern Chile.

Abstract

Pavement markings serve to convey information to drivers, regulate driving behavior, and effectively mitigate traffic congestion and reduce accidents. Nonetheless, due to traffic exposure and temperature stress, pavement markings may develop defects to diverse degrees. Consequently, the inspection and maintenance of pavement markings has been paid high attention. Traditional manual detection methods prove time-consuming, subjective, and present security risks. Therefore, we employed four object detection models [You Only Look Once version 5 (YOLOv5), YOLOv7, faster region convolutional neural networks (Faster R-CNN) with visual geometry group laboratory (VGG), and Faster R-CNN with residual network (ResNet)] to achieve intelligent recognition of pavement marking defects through deep learning. Each model underwent 1,000 epochs of training and utilized 2,000 annotated road inspection images. Through data augmentation, module optimization, and anchor redesign, these models can locate pavement markings and classify their defects. The accuracy and efficiency of the model were evaluated by mean average precision (mAP) and frames per second. In addition, we introduced evaluation indicators that focused on defect types to assist in selecting models with high applicability in detecting markings. Among these models, the optimized Faster R-CNN with VGG as the backbone network has an mAP of 93.96% and can detect over 28 images per second, which meets the engineering requirements.

Practical Applications

Pavement markings play a crucial role in guiding driver behavior, and they significantly contribute to alleviating traffic congestion and reducing traffic accidents. As the demand for autonomous driving technology increases, the maintenance of pavement markings has become more important. To address this, it is essential to determine which pavement markings require maintenance through inspection. However, traditional manual inspection methods suffer from issues such as low efficiency, high cost, and safety hazards. In this work, we developed four models utilizing artificial intelligence to automatically detect pavement markings in road inspection vehicle images. This approach significantly reduces the need for manual operation and ensures efficient and safe detection of pavement markings. The automatic detection results accurately match the actual position of the pavement markings, thus providing valuable guidance for maintenance work.

Staged construction is often used for bridges, in which a portion of a bridge remains open to traffic while another portion is closed for construction. Although staged construction reduces delays and the need for detours, subjecting the partially constructed bridge to dead and vehicular live loading can lead to damage and subsequent deterioration, including compromised longitudinal construction joints and transverse deck cracking. Despite past studies and best practices, transverse cracking persists and is often observed more on one side of the deck during construction, and no definitive explanation has been provided. As such, the objective of this study is to present a mechanics-based method for assessing transverse deck cracking in bridges during staged construction. Demonstrated on two bridges that underwent staged construction, the assessment method provides insights into the persistence of the issue and provides a mechanics-based explanation for observations of transverse cracking sometimes being observed only on one side of the bridge. The method, based on the superposition of strains, considers the sequence of steps in the construction process and estimates strain demands in the concrete decks at each stage, considering variations of the structural configuration, applied loads, and time-dependent behaviors. The results of the assessment agree with finite-element analysis results while requiring less model development and computational effort. The estimated strain history is then used to assess whether transverse cracking occurs by comparing the demand to the concrete’s tensile strength in time. The method is demonstrated on a new bridge constructed in stages and an existing bridge that underwent rehabilitation via staged construction. The results of the demonstrations suggest the importance of evaluating both the open and closed portions of bridge decks at each stage, as each portion has different structural and mechanical properties and loading demands. The assessment results are qualitatively validated based upon information from postconstruction bridge inspections. Furthermore, the method can be used to gain insight into mitigating transverse deck cracking and support decision-making for bridge construction practices.

Abstract

Masonry walls are commonly preferred in the construction of low-rise buildings due to their proven efficiency in resisting gravity loads and their relatively low material and labor costs. However, few studies investigating the behavior of retrofitted buildings under blast loading have been found in the literature. This paper describes the first comprehensive study investigating the efficiency of four retrofitting techniques for a low-rise masonry building under blast loading. The investigated retrofitting materials were textile-reinforced mortar (TRM), carbon-fiber-reinforced polymers (CFRPs), glass-fiber-reinforced polymers (GFRPs), and polypropylene bands (PPBs). Compared with regular reinforcing steel, these materials have light weight, high corrosion resistance, and perfect durability properties, in addition to their ease of application. A three-dimensional applied element model (AEM), which accounts for both geometric and material nonlinearities, was developed and validated to analyze an existing four-story masonry building under blast loading. Material nonlinearity was considered by including nonlinear models for masonry, mortar, TRM, CFRPs, and PPBs. Masonry blocks were modeled using brick elements, while the in-between mortar was modeled using spring elements. Validation of the AEM was by modeling masonry walls taken from the literature and comparing their results with counterparts obtained from experiments from the literature. Cracking pattern, displacements, support rotation, and load capacity for the investigated building with and without retrofitting using the four materials were compared. Costs were compared based on market prices. Retrofitting the case study building with TRM, CFRPs, GFRPs, and PPBs reduced maximum support rotation by 39%–98%, 28%–88%, 33%–92%, and 9%–84%, respectively, and increased blast load capacity by 35%–404%, 48%–91%, 83%–135%, and 26%–46%, respectively. The study revealed that retrofitting the investigated building with TRM provided the optimum behavior in terms of cost, crack propagation, and blast load capacity.

Practical Applications

Masonry walls are frequently used in low-rise buildings due to their efficiency in resisting gravity loads and having low material and labor costs. This study investigated the efficiency of textile-reinforced mortar (TRM), carbon fiber–reinforced polymers (CFRPs), glass fiber–reinforced polymers (GFRPs), and polypropylene bands (PPBs) to improve the resistance of low-rise buildings to blast loading. The adopted retrofitting materials have light weight, high corrosion resistance, and perfect durability properties. A numerical model analyzed an existing four-story masonry building under blast loading. Cracking, displacements, support rotation, and load capacity with and without retrofitting were compared. Costs were compared based on market prices. Retrofitting the studied building with TRM, CFRPs, GFRPs, and PPBs reduced maximum support rotation by 39%–98%, 28%–88%, 33%–92%, and 9%–84%, respectively, and increased blast load capacity by 35%–404%, 48%–91%, 83%–135%, and 26%–46%, respectively. The study findings recommend TRM in retrofitting masonry buildings to achieve the least cost and the fewest cracks in addition to the highest blast load resistance.

Facility maintenance decisions are essential to ensuring infrastructure can continue to meet mission objectives. To keep facilities and equipment operational during budget shortfalls, sometimes myopic maintenance strategies are used due to their immediate benefits despite their longer-term suboptimal effects on facility health. This effect is even more pronounced when making maintenance plans across multiyear horizons. Often, these multiyear effects require nonlinear modeling to capture their complex interactions. This paper explains how to frame such a scenario as a linear programming problem by utilizing a degradation model and a predefined set of maintenance actions, like repairing, replacing, or performing no maintenance on a component. Translating to a linear program has many benefits, like guaranteed optimality and shorter computation times. Case studies show how this approach improves outcomes across different scopes, ranging from a single facility to a portfolio of facilities. The results show significantly better utilization of resources in terms of the resulting Building Condition Index for the same budget.

In view of the low identification accuracy of the crack-detection technology of asphalt pavement under the current conditions of complex pavement (subject to strong light, water on the road, debris, and so on), an asphalt pavement crack-detection algorithm based on improved YOLOv5s was proposed by building the data set for asphalt pavement cracks. The first step was to make the following improvements to the original YOLOv5s model according to the characteristics of the asphalt pavement crack data set: The $k$-means++ algorithm was used to recluster the anchor points of the crack data set, and the initial anchor frame matching the crack characteristics of the asphalt pavement was obtained to replace the default initial anchor frame in the YOLOv5 original model. In the prediction part of the original model, the Convolutional Block Attention Module (CBAM) was added in the order of first channel and then space to improve the detection ability of the model to small cracks. The CIoU_Loss function was used as the regression loss function of the model to replace the GIoU_Loss function in the original model to improve the positioning accuracy of the anchor frame. The second step was to perform an ablation experiment on the improved YOLOv5s model. This would prove that each improvement scheme could increase detection ability without conflict. The final step was to compare the improved YOLOv5s model with various classic target detection models in the data set of this paper: the Crack Forest Data set (CFD), the Crack500 data set, and the Crack200 data set. The results showed that the detection of the improved YOLOv5s model on each data set was better than other target detection models. The [email protected] and mAP@[0.5:0.95] of this model on the data set of this paper were 90.58% and 56.08%, respectively, which were much higher than other target detection models. These findings indicate that the improved YOLOv5s model had better detection under complex pavement conditions and could provide a theoretical basis for the automatic detection of asphalt pavement cracks.