Journal of Performance of Constructed Facilities

Pedestrian-induced vibration (PIV) is often the most persistent issue affecting the floor serviceability of buildings. These vibrations may cause discomfort to occupants and may also adversely affect the performance of sensitive equipment residing on the floor. Resolving floor vibration problems in built structures often requires costly mitigation measures. Using monitoring techniques, a floor’s response to PIV can be evaluated. The purpose of long-term floor monitoring is to have a comprehensive insight into the vibration levels. Due to the associated costs and challenges, long-term monitoring is not common. The alternatives, short-term monitoring and controlled walking tests, may not reflect the actual vibrations of the floor but are easier to perform. By using confidence interval (CI) analysis, CI width analysis, and the Kullback–Leibler divergence (KLD) method to evaluate measured PIV from a floor, this study proposes a methodology for obtaining the sufficient short-term monitoring duration (MD) that is required to evaluate the long-term measured PIV with acceptable accuracy. Also, the appropriate percentile(s) for evaluating floor performance is investigated. Two methods are discussed for deciding which percentile to use when evaluating floor performance. The first method is based on selecting the probability of non-exceedance (PONE) according to the PIV guidelines, and the second method is based on the definitions of the types of vibration based on ISO 10137 curves. The sufficient MD is obtained from the relative error calculation of the results. The results of this research provide a more realistic and improved methodology for analyzing the vibration performance of floors.

This study determines the reason for embankment leakages of a large dam and demonstrates the importance of site evaluations, aside from numerical seepage and slope stability analyses, to assess whether the leakage observed in the embankment dam is related to seepage from the reservoir or from the groundwater that flows into the embankment. Using the software SLIDE, numerical seepage analysis was performed to determine the seepage pattern and pore water pressures. Results revealed that the calculated seepage exit elevation at the embankment’s downstream shoulder perfectly matched the measured elevation of the leak. Finite element method seepage analyses showed that reservoir water seeped beyond the dam’s chimney filter layer and intersected the downstream slope, which further clarifies that the filter has not functioned well. This study finally puts forth that the leakages are mainly due to the use of a silty sandy soil band together with MH and CH soils compacted excessively on wet side of the optimum moisture content. In addition, through numerical seepage analyses, pore water pressures in the embankment were also calculated for slope stability computations of the embankment. Slope stability analyses showed that safety of the dam slopes is not adequately reliable for even the existing partial impoundment and static loading cases. Thus, this study also suggests that the reservoir water should be kept in an existing partial level and not increased to its designed level. Eventually, the study proposes two remedial measures alternatively and evaluates the best solution.

This paper proposes a method of identifying damage to bridge structures based on the principles of displacement influence line and wavelet packet transform. Three damage identification indicators were constructed, including the deviation of displacement influence line (DDIL), the deviation curvature of displacement influence line (DCDIL), and the relative energy rate of wavelet packet energy spectrum (RES). Taking a continuous beam bridge as an example, a finite element model is established to analyze the differences in using three indicators to locate the structural damage position, and to explore the effects of factors such as damage degree, damage location, dynamic response type, and bidirectional traffic on the identification effect. Next, the Zhengzhou Taohuayu Yellow River Bridge is taken as a practical case for verification analysis, and the anti-interference ability and sensitivity of the RES damage indicator are discussed under the interference of noise factors. The results demonstrate that the three identification indicators have excellent performance in identifying bridge structure damage. Compared to the DDIL, the DCDIL shows a more obvious mutation at the damage location, while the RES curve improves the problem of poor identification effect at locations far from the midspan of the bridge by requiring response information from multiple points of the bridge. The RES curve, which was constructed using displacement as the input parameter, exhibits the best comprehensive identification effect. Additionally, it maintains its robustness even when subjected to the interference of bidirectional traffic and noise.

Facility managers often need to accurately predict the annual maintenance of their building roofs to develop reliable and cost-effective maintenance plans that maximize their performance and life expectancy. This article presents the development of a novel machine learning (ML) model using XGBoost to predict maintenance costs of ethylene propylene diene monomer (EPDM) roofing systems, and compare its performance to multivariate linear regression (MLR). The two models were developed in three main phases that focused on data collection and processing, model development, and performance evaluation. The collected data include 374 historical annual maintenance records of EPDM roofs that consist of maintenance cost, age, area, opening rate, and weather data. The performance of the two developed models was evaluated using four metrics: mean absolute percentage error (MAPE), accuracy, root square mean error (RMSE), and coefficient of determination (${\mathrm{R}}^2$). The outcome of this performance evaluation illustrates that the average accuracy of the ML model in predicting maintenance costs of EPDM roofs (88.20%) was significantly higher than the MLR model (68.30%). This highlights the original contributions of the developed ML model. The ML model has novel capabilities to provide much-needed support for facility managers to improve the accuracy of estimating the annual maintenance costs of EPDM roofs to ensure the development of reliable maintenance plans for this type of roof.

This paper reports comparative studies of the fire behavior of partially encased composite beams (PECBs), slim-floor composite beams (SFCBs), and fully encased composite beams (FECBs) using validated numerical models. The effects of parameters such as load ratio, concrete slab width, concrete and steel strength, and section dimension of the steel beam were considered. The findings indicate that the mechanical behavior of PECBs, SFCBs, and FECBs undergoes four distinct phases when exposed to elevated temperature, namely an elastic phase, an elastic-plastic phase, a plastic yielding phase, and a plastic instability phase. The failure criterion proposed by ISO 834 is more appropriate for predicting the failure of PECBs, SFCBs, and FECBs than those proposed by BS 476-20 and ASTM E119-18. The failures of SFCBs and FECBs under fire were both demonstrated to be the crushing of concrete and yielding of steel beam, whereas when PECBs fail, the steel beam yields, but whether the concrete of PECBs is crushed is determined by the load ratio. At elevated temperatures, the web of PECBs and SFCBs yields earlier than the lower flange. Additionally, the lower flange of PECBs and SFCBs has the potential to convert from a state of tension to compression. Increasing the web height and thickness or decreasing the load ratio can prolong the duration of plastic yielding phases and plastic instability phases, thus improving the fire resistance of PECBs, SFCBs, and FECBs. In addition, the fire resistance of PECBs, SFCBs, and FECBs are barely affected by the concrete slab width, steel strength, and shear connection degree. Although PECBs and SFCBs both have good fire resistance, additional measures such as welding the reinforcement to the web or applying a fire protection layer to the lower flange are required to meet 2-h fire resistance at a load ratio of 0.4. Moreover, FECBs can achieve 2-h fire resistance even if the load ratio is up to 0.8.

The management and knowledge of landfills are generally inadequate and could lead to significant catastrophes. This is particularly concerning for solid waste landfills due to their large size and potential to threaten nearby structures and residents in the event of landslides. In this paper, we investigated a backfill landslide induced by road construction in a coal mine slag landfill. Our analysis of satellite images and field surveys revealed that the alteration of the mounding morphology at the slope’s crest by construction triggered the landslide. We established an engineering geological model of the main sliding plane and utilized the discrete-element program MatDEM to reproduce the landslide’s development and predict its final accumulation pattern and impact range. Our study highlights the potential dangers of engineering construction carried out on relatively stable solid landfills. The combined in situ and numerical analysis research methods employed in this study can provide important references for risk assessment and treatment measures.

Mass-timber construction is becoming more prevalent in Australia and around the world. However, as these mass-timber buildings become taller, it is important to consider robust detailing against accidental damages to major load-bearing elements and thereby avoid potential progressive collapse scenarios. In order to better understand the robustness of typical mass-timber buildings, this study analyzed a typical 10-story midrise mass-timber building subjected to different progressive collapse scenarios. A scenario-independent approach was considered in the analysis, which includes the sudden removal of a load-bearing glulam column from the midrise mass-timber building. It was observed that when internal ties and bracings were provided, the robustness of the building was not affected as there was an alternative load path created. The orthogonal tie beam and diagonal bracing can transfer about 60% and 20% of the axial loads of the removed column to the adjacent columns, respectively. It was found that the building becomes vulnerable when no internal tie or bracing is available to transfer the accidental load. In this scenario, higher stiffness of the cross-laminated timber panel-to-panel joint can be adopted to improve the robustness of mass-timber building.

Large numbers of bridges have already suffered various types of damage but still operate all year round without proper treatment. Conducted primarily manually, the routine bridge inspections are ineffective in detecting potential damage in time due to a lack of relevant instruments and equipment, particularly modern measures. In this study, a rapid and intelligent bridge inspection system that integrates multiple modules and deep learning algorithms was established. First, the robot inspection equipment is established. Then, the You Only Look Once version 3 (YOLOv3) object detection algorithm is employed to classify four types of defects from the acquired data. Finally, an image segmentation algorithm is used to identify crack defects at a pixel level. Experimental results reveal that the proposed system can be effectively applied to accurately locate defects (e.g., cracks, spalls, exposed tendons, and free lime) and identify cracks at a pixel level on various types of bridges without affecting traffic.

Age has often been the only factor considered in predictive models of degradation. This study, however, assesses the influence on degradation of coastal exposure for concrete beams, level of utilization for rendered cement floors, and rainfall for timber windows. First, the difference between random data and data categorized on the basis of high or low levels of environmental factors was explored to establish whether they had a perceptible influence on degradation. Next, five types of models were explored for fitting the data and making predictions: namely Markov chain, multiple linear regression, simple neural network, deep neural network, and random forest. Among the environmental factors, coastal exposure on concrete beams had the greatest influence, while rainfall on timber windows the least. Random forest modeling was the most accurate and was also explored using the local interpretable model-agnostic explanation (LIME) technique, which revealed that the environmental factor effects were more evident during the mid-life of elements rather than at the early or late stages. Including environmental factors in degradation models in addition to element age will increase their accuracy and portability.

The erosion of the structural backfill behind bridge abutments subjected to water flow conditions representative of extreme and overtopping floods was examined through scaled water tank testing using physical hydraulic models. The erosion behavior of the unreinforced backfill materials, embankment material, and structural backfill was tested. Based on the observed patterns of erosion and embankment failure of these tests, the proposed construction practices, including geosynthetics and riprap, were examined in the laboratory with the objective of reducing erosion and the probability of failure during flood events. The input flow rate, flow velocity, and upstream and downstream waterheads were controlled and measured during the tests. The research results indicated that slope failure before overtopping can be mitigated by using a riprap on the slope. Geosynthetics, including geogrids and geotextiles, effectively prevented global failure during flood events if the reinforced core of the backfill was appropriately wrapped.

Abstract

This study presents an experimental investigation on the structural effects of an ignited fire occurring in a steel warehouse building with storage racks located at a part of an industrial plant. In order to judge whether the structure needs to be dismantled, repaired, or reused after the fire, an on-site postfire damage reconnaissance study was conducted, and postfire mechanical properties, such as tensile tests and Charpy V-notch impact tests, and chemical analysis of the structural steels used for the building, were determined depending on specimens removed from the fire-damaged structural steel members. The bearing system of the one story industrial steel building having a span of 25 m consisted of 12 fixed supported rigid gabled moment frames with rafters and 12 m high columns of H Section (HEA) 360. At the roof, the bracings of circular hollow section (CHS) $114.3\times 3.0$ and the purlins of C180 were used, while at the level of 8.9 m, crane beams were designed as HEA 600. From the tensile and impact tests, stress-strain curves, yield strength, ultimate tensile strength, elongation at fracture, and impact energy of the steel specimens were achieved. After all the results obtained from the tests performed at room temperature were evaluated, it was concluded that the structural steel elements affected but showing no distortions after the fire could be reused conveniently. In addition, the proposed approach to estimate the temperature exposed by the structural steels during the fire could have the potential to contribute to the existing relevant literature.

Practical Applications

In this study, it is experimentally explained what can be done about whether a steel structure needs to be dismantled, repaired, or reused after a fire event. First performing on-site damage detection and then taking samples from the structural load bearing system members’ chemical and mechanical tests should be carried out. Depending on the site investigation, it can be easily considered that the heavily deformed structural steel members become unusable and need to be renewed. From the tensile and impact tests of the structural steel elements affected but showing no distortions after the fire, their postfire mechanical properties are vital and should be ascertained. After all the test results were obtained, it can be concluded whether the structural steel members affected by fire can be reused, or need to be repaired or removed. In terms of ductility and elastic-perfectly plastic properties, it has been observed that after the fire the steel structural members showing no or even slight distortions can be reused conveniently. Thus, due to high cost of reconstruction of a steel building, a meaningful economy as well as acquisition in time can be achieved with this approach.

The structural performance of a damaged web opened beam repaired and strengthened by U-shaped jacketing with fiber-reinforced geopolymer concrete (FRGC) is evaluated. Reinforced concrete (RC) beams with openings in the flexure (BMS) and shear (BSS) are initially tested to their ultimate loading. The damaged beams are repaired using geopolymer mortar (GM), followed by strengthening using three-sided FRGC jackets. To assess the efficiency of the noble strengthening technique, portland-based cement mortar (PCM) and fiber-reinforced cement concrete are also used as an alternative jacketing agent. The test results indicate that FRGC material can effectively restore the structure to its functional use at the earliest possible time. The BMS-type jacketed beam exhibits improved stiffness with enhanced load carrying capacity 1.3 times higher than the original beam. However, the effect of FRGC jacketing is found to be more effective in enhancing the load carrying capacity of the BSS-type by 1.75 times higher than the original specimen. All jacketed specimens exhibit deflection hardening behavior with improved deformation capacity leading to improved ductility.

This study investigated the effect of skew angle on the failure load and structural behavior of Persian brick masonry barrel vaults using experimental and numerical methods. To this end, experiments were conducted to determine the mechanical properties of the materials and the vault’s behavior under gravitational loads. Due to experimental and financial limitations, scaled models of materials and structures were employed. The gravitational loading was applied at a uniform rate and in two modes, symmetric (at mid-span) and asymmetric (at quarter-span). Then a numerical model developed using the nonlinear finite-element method was validated in ANSYS code by comparing the numerical and experimental results using the Willam-Warnke failure criterion. In addition, the effect of four skew angles and three vault dimension (span-to-depth) ratios on seven different geometries of Persian vaults, such as semicircular, pointed (raised, ordinary, and drop), and four-centered (raised, ordinary, and drop) was evaluated. In all modes, the results showed that the skew vault had a lower load-bearing capacity than the nonskew vault. In other words, the skewness of Persian brick vaults reduces their failure load. Moreover, the semicircular and drop pointed brick vaults demonstrated the highest sensitivity (54%) and lowest sensitivity (20.92%) to skew angle (at 45°) and nonskew modes compared to other Persian geometries, respectively. Also, the raised four-centered shape has the highest failure load in the symmetric and asymmetric modes.

Abstract

The safety, integrity, and longevity of steel bridge elements are affected by various environmental factors, such as moisture, atmospheric pollutants, and temperature. Protective coatings of steel bridge elements are especially sensitive to the service environments (e.g., atmospheric environments, water environments) of bridges, as many environmental stressors may accelerate premature failures of coatings. By leveraging the data on both bridge-related features (e.g., bridge age, type of service under bridge) and environmental features (e.g., chloride, moisture, atmospheric pollutant, temperature), this study focuses on bringing a data-driven understanding of steel bridge coating deterioration patterns and how the service environments of bridges may impact such patterns. Steel bridge coating performance prediction (SBCPP) models were built based on multilayer perceptron (MLP)–based artificial neural network (ANN) algorithm to predict and assess the coating conditions of girder elements of steel bridges in Florida. The results show that the SBCPP model with the best performance can precisely predict steel bridge coating conditions with a mean absolute error (MAE) of 0.0807. As compared to the models that do not account for environmental features, the performance of the proposed SBCPP models was significantly improved. Shapley additive explanations (SHAP) analysis was further conducted to interpret and analyze the influences of input features on the performance of the SBCPP model. The study offers an effective decision-making tool that has the potential to benefit state transportation agencies by allowing for easier and more efficient analysis or prediction of steel bridge coating performance.

Practical Applications

This study proposes data-driven models that analyze and predict the coating conditions of steel bridge elements based on the data of bridge-related features (e.g., bridge age, average daily traffic, type of service under bridge) and environmental features (e.g., chloride, moisture, atmospheric pollutant, temperature), thus providing knowledge and insights on corrosion-induced coating degradation for steel bridges in Florida. The proposed models serve as the foundations for an automatic coating performance assessment and prediction tool that can help state transportation agencies and other bridge owners easily evaluate the coating conditions of their steel bridges and identify those bridges that are in critical maintenance needs. Inspection of coating conditions traditionally requires significant manual efforts, which are expensive, time-consuming, and cause safety concerns. An automatic tool based on the proposed models can quickly and easily analyze the element-based coating conditions while considering the impacts of the service environments. By using the tool, a bridge owner can quickly identify those bridges that require more attention or funding support and decide which bridges should be given priority in terms of inspection, maintenance, and/or repair. Hence, acting as the foundation of such a decision support tool, the proposed work has the potential to reduce field-level coating inspection costs, efforts, and risks and enhance the efficiency of maintenance and repair-related decision-making.

An existing hydroelectric facility affected by alkali–aggregate reaction (AAR) is considered in this study through numerical and experimental investigations. A new developed phenomenological hygro–chemo–mechanical approach for AAR is presented and is shown to be practical and effective in damage assessing of large-scale hydraulic concrete structures. Experimental investigations were conducted to characterize the AAR kinetics of mass concrete mixture with very large aggregates, used in the construction of the facility. By combining experimental, numerical, and monitoring investigations, it was possible to assess size effects related to the free AAR expansion of mass concrete. Numerical simulations allowed to obtain a damage pattern similar to the cracking profile of the existing facility, to provide mechanical interpretation of the jumps observed in the instrumentation data, and most importantly, to predict the remaining time before reaching the asymptotic expansion of the facility: a very important result for owners of hydroelectric facilities affected by AAR.

This study focuses on the measurement of concrete cracks in three dimensions (3D) using the particle swarm optimization-based projection algorithm, OpenCV technique, and X-ray computerized tomography (CT) images. The primary objective is to visualize the development of cracks in fire-damaged concrete. The data for X-ray CT imaging were collected, consisting of 3,011 CT images obtained from damaged specimens. The sampling criteria employed ensured a convenient sampling process with a 95% confidence level and 5% limits of errors in the 50–50 category. The results reveal that the undamaged specimens exhibited an average of 406 smaller-sized pores, whereas the fire-damaged specimens, which were subjected to a temperature of 900°C for 1 h, displayed a significant increase in the number of pores and cracks. The proposed method successfully enables the plotting of 3D measurements of concrete crack development. This achievement has practical implications for practitioners, as it allows for (1) visualizing crack development as an initial step toward analyzing deterioration patterns in concrete, and (2) conducting an analysis of concrete failure in structures following fire scenario simulations. Overall, the findings of this study contribute to a better understanding of concrete crack development and offer valuable insights for practitioners in the field.

Sudden water inrush disasters in long, deeply buried tunnels in karst regions not only seriously threaten the safety of tunnel engineering structures but are also geological disasters that are difficult to prevent and control. To achieve efficient and rapid treatment of water inrush disasters in tunnels by in-depth analysis of the causal mechanism of sudden water inrush in a long, deeply buried tunnel, an efficient treatment countermeasure, grouting plugging materials, and control measures of slurry leakage are developed. The results show that faults and fractured evaporite solution breccia are highly water permeable, and the excavation of a tunnel disrupts the groundwater flow direction. Gravity causes groundwater to gather in the weakest part of the tunnel, causing a water inrush disaster. To prevent a tunnel water inrush disaster, the countermeasure treatment of pretreatment with plugging of the water inrush site, grouting pre-reinforcement, water inrush plugging, and local reinforcement is proposed. Plugging materials suitable for the different characteristics of water inrush have been developed. These materials have a good effect on the narrowing and plugging of water inrush points. Shallow plugging techniques are proposed for the phenomenon of slurry leakage in the treatment process of water inrush disasters. Field displacement monitoring and similar engineering applications verify the effectiveness of the treatment countermeasures and grouting materials. This work provides a reference for the treatment of tunnel water inrush disasters.