Journal of Performance of Constructed Facilities

Due to the near blast condition, columns on the front face of the building may snap off in the blast load’s initial state, leading to a condition of the blast load and progressive collapse effects occurring simultaneously. Herein, the damages caused in the building produced due to progressive collapse under blast loading scenario are investigated. A nonlinear time history analysis of the three-dimensional (3D) model of a six-story building is performed for the progressive collapse following General Services Administration (GSA) guidelines. The surface blast effect is included using simulated air pressure and ground motion produced due to the blast. The progressive collapse analysis is performed for two cases, namely, (1) considering blast effect due to surface blast; and (2) neglecting the effects of the blast wave, and the results of the two cases are compared. The study results show that the effect of the duration of the transient loading used for the progressive collapse analysis has a significant influence on the response quantities of interest. Further, all response quantities of interest are considerably increased when the blast effect is considered compared to the case when the same is neglected.

Nearly all aluminum overhead truss sign structures (OHTSSs) in Kansas constructed before 2015 use an identical type of hot-riveted coupling assembly to provide connections between vertical poles and horizontal truss members. Although many of the connections have been in service for 30 to 40 years, no research has been conducted to determine their fatigue performance. This paper presents a study aimed at characterizing the fatigue performance of the coupler connections. The study includes a series of finite-element (FE) analyses and 24 experimental fatigue tests. The FE analyses were used to determine design-level fatigue demands on the coupler connections and to provide information for designing the experimental setups used in this test program. Four sign structures with three connection properties and six loading modes were modeled. The experimental tests were conducted to characterize the fatigue performance of the connection. Tests were conducted for three loading modes with respect to the load on the rivet connecting the two interior half-couplers: (1) tension, (2) vertical shear, and (3) horizontal shear. Results indicated that when loaded below 10 Ksi (68.9 MPa), the connection can be classified as a Category E′ based on stress in the rivet. The FE analyses and test results indicated that fatigue failures are unlikely to occur in the coupler connections used in OHTSSs. In addition, two used coupler assemblies from OHTSSs taken out of service were also tested and the failure surfaces were examined under a microscope. No reliable evidence of preexisting cracks was found.

The use of plain (smooth and undeformed) rebars as longitudinal and transverse reinforcement in RC members is one of the most encountered practices in old construction technology. Thus, probable vulnerabilities due to plain rebar implementation should be considered in the seismic evaluation of RC buildings. It is known that reduced bond stresses distributed along the plain rebar surface due to the absence of the ribs leads to a resultant reduction in effective stiffness and an increase in rotation capacities for RC members. Therefore, RC buildings that were constructed according to the older design codes using plain rebars were expected to deform laterally beyond the anticipated deformation capacities that were determined based only on the RC members reinforced with deformed rebars. In this study, the modeling strategies were examined and verified with the results from an existing building test in the literature, and then the seismic performance of a five-story RC framed building, which was reinforced with plain rebars, was evaluated under nonlinear time-history analysis (NTHA). In order for code comparison, the nonlinear characteristics of the RC members were determined according to the previously suggested model for members reinforced with plain rebars and the current Turkish earthquake code regulations. The parameters investigated were (1) the number of members at each damage state, (2) the maximum interstory drift ratios, and (3) the normalized story shears by building weight considering the earthquake type (pulse type or non-pulse type) and predamage effects. The analysis results emphasized the importance of rebar characteristics in seismic performance assessment, because the use of plain rebars reduced the number of plastic hinges while leading to higher drift ratios. This phenomenon is analogous to the behavior of RC beams reinforced with plain rebars which fail with a smaller number of wide cracks rather than a greater number of narrow cracks as in the case of deformed rebars.

This paper introduces a method of strengthening prestressed concrete continuous box-girder bridges with prestressed steel strands. Construction process monitoring, field load tests before and after reinforcement, and health monitoring after reinforcement were performed to verify the effectiveness of the external prestressed steel strand reinforcement method. During the construction monitoring process, the measured values of each tensioning step are basically consistent with the theoretical values, but the measured values are slightly less than the theoretical values. The results of the field load tests show that the bending strength and stiffness of the strengthened girder have been improved. During health monitoring, the strain value and deflection value data for some parts across the specified section were collected. According to the data, the strain and deflection values fluctuated within the specified limit, and the bridge was always in a safe state.

The purpose of this research was to develop a computational tool to ease the process of technical building inspections. Thus, a web-based application was created, supported by a well-structured expert inspection system for the building envelope. This application has a distributed architecture, which allows easy implementation using common technologies. Java and JavaScript programming languages are used, as well as HTML and CSS for the interface. The building inspection application is composed of three main processes, namely: a module to treat correlations between data referring to building pathology (e.g., classification of defects and their causes and the correlations between them); a process to help collect information during the inspection procedure (data entry forms filled dynamically); and a process to update the information stored in the first process (for instance, to include new material in the system). The application is extensible and is capable of inspecting 12 types of building elements and materials used in the building envelope. The first demonstration prototype allows a complete inspection of two types of materials. Tests of the application have revealed the advantages of transposing extensive building pathology information to a computational platform: saving time (no need to insert data after the inspection); guiding the surveyor throughout the inspection (the surveyor’s input options are limited); providing an easy-to-use interface, which runs on affordable devices; allowing direct storage of information; and reducing errors and omissions of inspection data, as the system guarantees its consistency. In the future, new functionalities can be developed to improve the application’s capabilities.

Under excessive plastic deformations, pitting corrosion can accelerate ductile fracture initiation in steel structures. For an accurate numerical prediction of ductile fracture in corrosion pits, a micromechanical fracture criterion along with a fine three-dimensional solid meshing is required. Previous studies on this topic are limited to simple plates; however, for a more detailed component, e.g., steel beam-to-column joint, implementing the pit geometry on the global model of the joint is challenging in terms of meshing and computational time. In this paper, two-level numerical modeling was employed to reduce the complexity of the problem. In this technique, submodels with refined mesh are used to perform micromechanical simulations and assess the ductility degradation of joints. For a case study joint, the pits near the edge of the web and flange plates were found to be the most critical and they can reduce the fracture initiation displacement of the joint by about 25%. On the other hand, the pits located on the edges of plates or far from the edges caused a negligible reduction in the fracture initiation displacement of the joint. These results suggest two-level numerical modeling as a viable technique to facilitate micromechanical simulation of pitting corrosion in corroded steel joints.

Asset performance evaluations serve as a benchmark that can be used to make asset manufacturer selection decisions, i.e., choosing the brand of manufacturer that provides the highest performance among all brand competitors. Understanding the environmental conditions to which assets are subjected provides facility managers another data point to understand asset performance. This research builds upon a previously published performance-based manufacturer selection metric by investigating the linkage between asset performance and exposure to local climate, using chiller and air handler data from 20 United States Air Force installations. The link between environmental factors such as Heating Degree Days (HDD); Cooling Degree Days (CDD); Solar Irradiance; and a variable that accounts for relative humidity, and asset performance is investigated using analysis of variance (ANOVA) testing and correlation coefficients. The results reveal that most assets, regardless of location, (1) possess a moderate to strong performance; and (2) cumulative climate exposure and asset manufacturer selection influence asset performance. This work highlights the need for facility managers to consider the influence of climate on technical performance and use it as a decision criterion in manufacturer selection.

Hangers are primary load-carrying members in suspension bridges, connecting the girder to the main cable, and in case of fire accident, the bridge safety may be affected due to the deteriorated performance of hangers. In this study, a number of 160-wire specimens were tested to obtain their strengths after being heated to various temperatures and cooled down. Then the Weibull distribution was used to describe the randomness of the results. Thereafter, a statistical model was established to estimate the capacity of postfire hanger consisting of parallel steel wires. Subsequently, static tensile tests were carried out on three steel wire bundles, with each consisting of 19 parallel steel wires, to verify the model, where two bundles were heated. Based on the predicted residual capacities and field measured hanger forces, the reliabilities of postfire hangers of the Runyang Suspension Bridge were assessed. It was found that the tensile strength of hangers degraded after exposure to high temperatures, and their residual strength and reliability were highly dependent on the highest exposure temperature. Finally, the repair and replacement of the four postfire hangers on the bridge was conducted according to the assessment.

This paper reports an assessment of structure damages after the Richter magnitude 6.4 Puerto Rico earthquake that occurred on January 7, 2020. A review of the seismicity of the region and the postdisaster investigation of structural damages is first presented. The paper then focuses on explaining the failure modes of 2-story residential structures with open first story, which demonstrated the classical soft story behavior. The typological characterization of the failure modes is investigated using a nonlinear elastic dynamic analysis with a single degree of freedom model, which illustrates the contributions from partial to full construction of infill walls to the stiffness of the 2-story structures. The results indicate that the completion of the infill walls can significantly improve the stiffness of these structures and reduce damages to the structures.

The performance evaluation of truss bridges is very difficult owing to their complex structure and numerous components. This paper develops an evaluation framework for truss structures using the internet of things (IOT) technique and proposes a novel methodology to increase evaluation accuracy based on cloud matter element fusion (CMEF). An experiment was carried out on a laboratory truss that was equipped with monitoring devices. Then the monitoring system, data storage module, and loading equipment were connected by IOT and then transmitted to the data processing module to carry out real-time evaluation. Furthermore, in the data processing module, the cloud matter element can effectively integrate multiple evaluation features into a global evaluation system, and the randomness contained in the monitoring data and the fuzziness inherent in the evaluation process can be considered synthetically by the cloud models established in this study. Then these evaluation features with their respective weights are fused by evidence fusion, so as to obtain the final performance of the experimental truss. The results have indicated that the evaluation accuracy can be obviously increased from 46.1% to 99.5% by the proposed methodology. The proposed method in this study provides an illustration of an effective evaluation for the truss structure.

Artificial intelligence systems based on fuzzy logic models can manage both data obtained in situ from the buildings and that obtained from the knowledge base of professional experts. These kinds of digital methods can optimize resources and manage the uncertainty associated with the evaluation processes conducted by one or even different professional experts. Digital management systems that focus on preservation strategies help harmonize the results, as slight variations in the evaluation process of input variables do not generate a significant dispersion over the model’s output variable. The preventive conservation of heritage buildings requires cooperative efforts in terms of the definition of heritage value and in the establishment of future preservation strategies, where qualified experts’ opinions are key to examining multidisciplinary knowledge. In this respect, the incorporation of new protocols, which can help decision makers prioritize interventions and avoid restoration actions that are unnecessarily invasive or might cause irreparable loss of the properties, is urgently needed. The aim of this study concerns the analysis of the heritage value of the Mutizaval House emplaced in Purranque (South Chile) and a detailed examination of the functional degradation condition regarding each space of the whole building. The application of the fuzzy logic model revealed that 13 spaces out of 22 were ranked Condition C. Therefore, the methodology recommended monitoring the building’s state of conservation and implementing a preventive maintenance action within no more than five years to preserve the building’s features in a reasonably good state of functional condition. This kind of new digital management tool can be useful to stakeholders who are responsible for the establishment of preventive maintenance programs for heritage buildings in Chile.

Temporal discounting is a cognitive behavior with important implications in preparing for high-impact and low-probability events. Evaluation of live-load effects has a great influence on the design, maintenance, and rehabilitation of bridges in the US. This study employed statistical methods to evaluate high-impact and low-probability bridge overloading events and proposes a cognitive approach to evaluating live-load factors because stakeholders discount the probability of observing overweight vehicles based on Kahneman and Tversky’s prospect theory. This paper quantified the likelihood of observing extreme weights on bridges from 10 Weigh-In-Motion sites in Georgia using the extreme value theory. A sensitivity analysis showed how predicted live-load factors vary in response to the choice of a threshold or an extreme percentile. Subsequently, the process of predicting maximum live-load factors was validated using another state’s data. It was concluded that a live-load factor is affected by a shape parameter and is numerically quantifiable for each site, and that near-term live-load factors are more salient for preparing bridges for high-risk, low-probability overloading events.

The quality of the building stocks of most Middle Eastern countries is affected by earthquakes produced by the East Anatolian fault. On January 24, 2020, a medium-magnitude earthquake struck the Elazığ Province in eastern Turkey. The observed damage to structures was found to be excessive because of the poor-quality building stock of the region. In this study, the seismic behavior of the building stock of Elazığ after the January 24, 2020, Sivrice earthquake was discussed in detail. The reader was first provided with some insight regarding the Elazığ province. Next, the seismic behavior of the region and past earthquakes that occurred were examined to highlight the high seismicity of the region. Further, strong ground motion parameters and the hazards of the region were evaluated in detail, and the results of the reconnaissance studies were presented. Then, the reasons for the observed damage considering reinforced concrete and masonry structures were presented, and the field applications and past construction techniques were compared with the recent regulations established in 2018. Finally, rebuilding and resettlement activities after the earthquake were discussed in detail. From this study, it was found that the damages of structures in an earthquake-affected area can be attributed primarily to the design stage and improper control mechanisms employed during construction. Based on field investigations, poor-quality civil engineering applications on both reinforced concrete and masonry buildings were found to be fundamental factors that affected the seismic behavior of the considered structures. Rural regions were found to suffer extensive damage after the earthquake. The results suggest that excessive loss could have been prevented if required care were given to the construction practice. This study is expected to help improve the understanding regarding the seismic behavior of low-quality structures, and it is expected to encourage developments that will help enhance the seismic capacity of existing buildings in the region.

Detecting the corrosive environment of reinforced concrete (RC) bridge decks is of critical importance for evaluating the reliability and safety of bridge structures. However, accurately and automatically detecting a corrosive environment with traditional methods is challenging. This paper proposes a method for the automatic corrosive environment detection of bridge decks from ground-penetrating radar (GPR) data based on the single-shot multibox detector (SSD) model. This method can be divided into three steps: data preprocessing, automatic rebar picking, and corrosive environment mapping. First, the GPR data are preprocessed to enhance the contrast of the hyperbolic feature in GPR B-scans. Then the rebars in the B-scan images are automatically picked up by the trained SSD model. Finally, the corrosive environment contour map of the bridge deck is generated with the rebar reflection amplitudes after depth correction. The SSD model was trained with 10,316 B-scan images and tested with 2,578 images. The $300\times 300\text{-}\text{pixel}$ B-scan image typically included three to five hyperbolas. A case study with GPR data from a tested bridge was employed to validate the feasibility of the proposed method. The results show that the accuracy of the automatic corrosive environment detection method can reach 98% and is considerably higher than that of commercial software methods.

Because it is impossible to predict the temperature in advance, specific fire scenes (fire type, fire location, tunnel geometry, etc.) are unknown in traditional tunnel fire research. To address this difficulty, this work developed a novel algorithm to achieve temperature prognosis in tunnel fires that includes an updatable backpropagation (BP) neural network and a smoothing procedure. The data-driven algorithm is not limited to a specific fire scene, which makes it easy to fit real complex tunnel fire disasters. In addition, a full-scale fire test was conducted and utilized to verify the algorithm. Two innovations, including the updatable BP neural network and the smoothing procedure, made the predicted results match well with the experimental results. We can achieve a real-time precise temperature prediction 20 s in advance at a high accuracy of about 85.6%. If there is no sudden external factor intervention, the accuracy is about 99.4%. The algorithm provides an effective numerical tool for early fire warning and firefighting decision making that can address the temperature prognosis of tunnel fires.