Journal of Performance of Constructed Facilities

Markings are essential elements of roads and are important for driving safety enforcement. Traditional detection of marking defects relies on manual inspection, which is less effective. However, few studies could be found focusing on automatic detection of defective road markings. In this study, a detection method for defective road marking is proposed based on unmanned aerial vehicle (UAV) photography. Image acquisition based on UAV photography was discussed. The detailed processes of image preprocessing, road segmentation, marking extraction, and defective marking detection are presented. The proposed method was tested with images collected from highways, normal urban roads, and poorly maintained urban roads in Nanjing, China. Overall, the proposed method shows good robustness in defective marking detection. The precision and recall of marking extraction on highways and normal urban roads are over 93%, and the results on poorly maintained urban roads are about 76%, due to the poor road conditions. The precision of damage identification on lane markings and arrow markings were all greater than 90%. The proposed automatic process is capable of accurately and quantitatively detecting the sidelines, lane markings, and indicator markings.

Tuned mass dampers (TMDs) are widely used to control excessive wind-induced vibration in the box girders of long-span bridges. Although the optimal design of TMDs has been investigated abundantly in the last few years, the effectiveness of TMDs in use has not been thoroughly studied. In this paper, a method combining a machine learning (ML)–based approach is developed to evaluate the TMD effectiveness. The theoretical formulation and the flowchart of the method are firstly presented, which utilizes characteristics of TMD vibration amplitude, phase shift between TMDs and the bridge, and the mode resonant frequency component. Seven commonly used ML techniques, i.e., artificial neural network (ANN), decision tree (DT), $k$-nearest neighbors (KNN), random forest (RF), adaptive boosting (AdaBoost), gradient boosting regression tree (GBRT), and extreme gradient boosting (XGB), were adopted to generate the predictive models, and the structural health monitoring (SHM) data of the bridge were used as the training data. The wind properties and temperature were set as the input, and the TMD accelerations are set as the output. Meanwhile, the Shapley Additive Explanations (SHAP) was adopted to identify the influences of the input variables on the TMD’s performance. The result indicated that the proposed method is reliable to evaluate the effectiveness of the TMDs, and it was shown that wind velocity is the most important parameter. BecauseTMDs are often widely used to control vibration in bridges, the proposed ML-based method can be used as an effective tool to assess and/or cross-check the effectiveness of TMDs.

This paper presents the results of damage surveys in Mirpur, in the Azad Jammu and Kashmir region of Pakistan, which was affected by a moderate earthquake on September 24, 2019. The epicenter of this earthquake was near the city of Mirpur at a shallow depth of no more than 10 km. Rupture of ground surface was observed in the area close to the epicenter, which can be associated with the release of seismic energy at shallow depths. The ground acceleration recorded at the site close to the epicenter was influenced by the rupture directivity effects. A significantly high vertical component of ground acceleration was observed at a site in close vicinity of the epicenter. Ground-motion amplification was also indicated by the data analysis in the presence of soft soil. A few low- to medium-rise reinforced concrete buildings were severely damaged by the ground shaking. The columns in these buildings experienced compression or shear-compression mode of failure in the presence of high axial compression applied by the vertical seismic forces and/or overturning moment. The longitudinal bars in the columns also buckled due to high axial compression demands coupled with insufficient confinement in the plastic hinging regions.

Imperfections or uneven surfaces of welded areas due to uncertain errors such as equipment, weld quality, or mechanical damage may cause defects or anomalies such as corrosion or cracks and lead to other failures such as burst or leakage. Corrosion tends to occur at the uneven area due to the stagnation of the hydrocarbons for a long period, which directly can reduce the strength of the pipeline itself. This paper proposes a framework that incorporates structural reliability analysis (SRA) to analyze the safety and integrity levels of corroded girth welded API 5L X56 pipeline. By developing the limit state functions (LSFs) with reference to previous studies and existing codes and standards, the assessment was divided into two sections: assessing the pipeline as a single pipeline, and as sectional parts (girth welded area) of the pipeline. The probabilistic evaluation was carried out using Monte Carlo simulation (MCS), and the obtained results were compared in terms of the failure probability, $P_f$. The results demonstrated that two burst pressure models—the Shell-92 and Zhang et al. models—are highly conservative based on the failure probability, whereas the pipeline corrosion failure criterion (PCORRC) gives the most reasonable results for both general corroded and girth welded pipeline scenarios. The findings of this work also indicate that the failure probability in sectional pipes is more reliable than that in entire pipes due to the consideration of corrosion distributions in the pipeline.

Water distribution networks (WDNs) are complex interconnected networks that require extensive planning and maintenance to ensure good quality water is delivered to all consumers. The main task of WDNs is to provide consumers with a minimum acceptable level of supply (in terms of pressure, availability, and water quality) at all times under a range of operating conditions. However, the water infrastructure in North America signifies an urgent need of upgrading the aging and deteriorating distribution systems if they are to continue to provide consumers with reliable and safe water supplies. In this context, this research is proposing a new reliability model for assessing the mechanical/structural as well as hydraulic conditions of a WDN to identify failure-prone components and prioritize their renewal. The developed model provides a systematic and practical methodology to calculate the mechanical/structural reliability of the pipe and its accessories (valves, hydrants, and so on) through the consideration of historical failures of components. The research deployed pressure-dependent demand analysis to determine the hydraulic reliability of the network in meeting the pressure requirements and overall hydraulics of the network. The minimum-cut-set theory was implemented in both reliability assessments, and the outputs of the two models were integrated to provide a representative reliability of the network. A sensitivity analysis was followed to study the change in segments’ attributes on the mechanical/structural and hydraulic reliabilities. The approach was implemented on the City of London, Canada, network (north and south) to test its applicability. The developed model is expected to assist decision makers in integrating the failure records with the hydraulic simulation to plan for optimum intervention actions.

The object of this research was to identify the list of climatological variables involved in the appearance of construction failures in the external walls of dwellings through the analysis of over one thousand cases. The data source used consisted of the judicial records of the Justice Administration, a source to which few researchers have access, given the dispersion of the data and the permissions required to access it. Once obtained, all situations pertaining to dwellings were read and annotated, until 100% of the cases were accounted for, and percentages of recurrence were calculated for each of the nine different types of failures that were described. A study was carried out by so-called strips of climatic location according to four climatological variables (situation, latitude, climate, and annual rainfall) that were sorted from largest to smallest to obtain the ranks of pathology concentration according to the resulting preponderance. Using these results, technicians will be able to identify the most problematic climate-geographical areas by determining the ranks of normalized frequencies, allowing them to take the necessary measures during the construction process. The lessons learned can be incorporated into maintenance plans to optimize preventive maintenance frequency and actions.

Stone arch bridges are symbols of a shared civil engineering heritage worldwide, but they suffer from damage caused by various natural disasters that have occurred during their long service. The basis of ancient bridge preservation is the current situation assessment. This study proposes a structural parameter identification method for stone arch bridges via three-dimensional (3D) laser scanning. An automatic algorithm of arch axis extraction from massive point cloud data is developed and applied to the 800-year-old Lugou Bridge (LGB) in Beijing, and its structural parameters are identified from the extracted arch axis. The results reveal that the LGB’s arch axis is close to the arc curve. The axis extraction is accurate and the identification method is reliable, when compared to field measurement. The relative deformation between the upstream and downstream sides of each arch and the relative deviations of the clear rise and span between two longitudinally symmetric arches are manifestly large. This study extends the current identification methods for bridge heritage based on a 3D point cloud and provides a useful basis upon which to protect ancient arch bridges.

The typical approach to modeling building degradation is based on consecutive visual inspections using generic deterioration ratings to characterize the conditions of elements. Our research used snapshot data to capture a range of ages efficiently, and very clearly defined element-specific deficiency-based conditions to reduce subjectivity. Snapshot data were obtained of 12 distinct building elements, from buildings owned by 7 local councils in Sri Lanka (with ages up to 60 years), using both generic deterioration– and deficiency-based condition ratings. The deficiency-based ratings were found to be lower than the generic deterioration–based ratings at early ages but higher at later ages. Two data-driven indicators were used to estimate the relatively maintenance-free life of each element. Markov degradation models were developed for both types of ratings. The deficiency-based predictions were found to be more accurate than generic deterioration–based ones. Slabs, beams, and columns had the lowest rate of degradation; timber doors, timber windows, ceilings, wall plaster and floor tiles had higher rates; and ceiling fans, fan regulators, wall paint, and rendered cement floors had the highest rates.

The present study evaluates the performance of two hillside building configurations, i.e., split foundation (SF) and step-back (SB), commonly observed in the seismically active Indian Himalayan region, under blast and blast-induced ground motion using an uncoupled approach. Force-controlled nonlinear static analyses are carried out to probabilistically assess the critical blast location for bare- and infill-framed buildings. It is observed that the blast on the downhill side is critical for the hillside buildings having a story ratio of 0.5; whereas, the blast on the uphill side is more damaging for the buildings with a story ratio of 1 and 2. The blast fragility curves, developed herein, show that the presence of infills significantly affects the blast response of RC hillside buildings and the location of the peak response. Further, critical scaled distances corresponding to various damage states for the hillside buildings are found to be more than that for the regular buildings. Furthermore, the response of hillside buildings was investigated under blast-induced ground motions to evaluate the key engineering demand parameters pertinent to structural and non-structural damages.

Corrosion is one of the most common defects of buried pipelines. Accurate prediction of the maximum pitting depth of corroded pipelines is conducive to assessing the remaining strength of the pipeline. In the context of big data, machine learning has been proved to have good results. However, previous studies have less consideration of feature selection in modeling, so that the interpretation of corrosion mechanism in machine learning model is not clear enough. This paper aims to develop a novel intelligent framework to accurately predict the maximum pitting depth of buried pipelines. The framework utilizes correlation analysis to extract features from many factors related to corrosion depth, and then uses a hybrid machine learning tool to predict the maximum pitting depth. Through empirical analysis, it is found that the pipe age of buried pipelines is the leading factor for pipeline corrosion. The prediction model proposed in this paper uses an improved gray wolf optimizer to optimize the support vector machine, and compares the prediction results with eight other benchmark models. It is concluded that the proposed model has the best prediction accuracy and stability. Finally, this paper discusses the influence of feature analysis on the prediction results, showing that this operation can effectively improve the model’s prediction performance and enhance interpretability.

The subway is one of the most important public infrastructure systems able to accommodate huge numbers of passengers daily and across the world. Hence, governments are always obliged to provide and secure a large amount of funds to maintain the required levels of performance. Nevertheless, the majority of current operating subway networks have faced significant deterioration in their provided services. This deterioration is not just confined to the unavailability of sufficient funds from governments, but also to the absence of comprehensive maintenance plans and a lack of documentation of past maintenance. In the same manner, governments are facing problems securing sufficient funds to maintain the operational efficiency of subway networks. This research presents a model for the condition assessment and budget allocation optimization of subway operating systems. The model incorporates technical maintenance information and maintenance progress metrics for each subway component defined on a building information modeling (BIM) model. The output of this model is utilized by BIM as an interoperable platform to regulate maintenance action documentation and track the actual maintenance progress status in subways. The selected operating systems were identified based on their contribution to the level of service. These systems include overhead catenary, communications, rolling stock, railway tracks, ticketing, lighting & power supply rectifier stations, and centralized control. A BIM-based model was developed to integrate the optimized maintenance plans with the modeled subway components and to compare the actual maintenance practices with optimized planned actions. Cairo Metro line 1 was adopted as a case study to showcase the main characteristics of the developed model. The results of the developed model reveal proper workability represented by running the developed coding scripts that correctly reflect the segregated maintenance plans into schedules integrated with the developed BIM model.