Journal of Performance of Constructed Facilities

Various applications of incinerator bottom ashes (IBA) have been considered sustainable in many countries for years. This study evaluated the suitability of applying IBA added pervious concrete as roadway base course. Material properties, mix proportion, and an in situ test section were evaluated. The IBA were found to have good mechanical properties compared with natural aggregates. The results of the mix design indicated that increasing IBA will lead to a reduction of compaction strength. It was found that mixtures with less than 60% IBA replacement can give acceptable performance that meets specifications. Adding IBA to pervious concrete does not reduce its volumetric stability. In situ test results indicated that the permeability and noise of test sections with IBA are better than those of traditional asphalt concrete pavement. Roughness and rutting of test sections with IBA are similar to, although slightly worse than, those of traditional asphalt concrete pavement. Water samples showed that IBA-containing pervious concrete had only slight Cu leaching, which was much lower than the limits permitted by standards. It was concluded from all the test results and observations in this study that applying IBA-containing pervious concrete as roadway base course is adequate under the given conditions.

With the development of deep underground space, there is an ever-growing demand for the excavation of deep foundations in the urban zone. The diaphragm wall is the most commonly used retaining and waterproof structure. Therefore, the quality of the diaphragm wall plays an essential role in the environmental impact and security risk control of deep foundation excavation. Consequently, it is required to develop practical ways to detect defects in diaphragm walls. It is critical to localize defects before excavation and characterize them in detail for proper countermeasures. Unfortunately, up to now, the traditional detection methods are not yet available for professional use. This paper presents a contribution to filling this gap based on cross-hole sonic logging (CSL) and ground-penetrating radar (GPR). First, CSL is carried out prior to excavation through detection tubes preinstalled in the wall. Second, Bayesian inversion is used to obtain a high-quality two-dimensional (2D) visualization of the defects, and the depth and width can be obtained before excavation. Third, GPR is applied to precise detection when excavating to the depth of the defect. Finally, by employing an advanced bifrequency back-projection method, the correlation between two domain frequency radar data is used to obtain the three-dimensional (3D) characteristics of the defects. The proposed method was used at a 40-m level of deep excavation in Shanghai, China, and allowed engineers to determine the location and 3D characteristics of defects. An identified analysis of the defects with this method showed a reasonably good match with manual observation after excavation.

Identification of noise signals is one of the most challenging problems in health monitoring of a bridge structure using acoustic emission (AE) monitoring and identification technology. Hardware filtering technology and spatial identification technologies are the most common methods used in identifying signals from the defects of the bridge, which have great limitations due to the presence of environmental noise. Therefore, this paper focuses on the AE noise signal from a bridge in an operation state and other specific loading states, which is diagnosed in the hardware filtering technology, spatial identification, and self-organizing map (SOM) neural network, to obtain the new noise recognition methods. It is found that the first two methods can indeed filter the noise signal, but the filtering rate can only reach about 50%, and can barely filter strong noise signals. The SOM neural network had strong self-recognition ability. The classification accuracy of simulated AE signals is 90% and 100%, respectively. The trained network was used to test 183 sample signals, and the defect signal detection accuracy reached 76% and 78.8%; therefore, the noise signal filtering effect is significantly improved.

Doors are the most critical part in any structure as they may eventually fail first when subjected to extreme loads such as blasts. Blast resistant doors in a structure work as effective barriers against blast pressure thus making the entire structure safe. Accordingly, the need to provide blast resistant doors arises in important structures to endure blast pressure. Herein, the comparative performance of a three-hinged five-latched double leaf composite door subjected to blast loading is investigated using finite element analysis software LsDyna. The composite door consists of front and rear steel plates composed of armor steel and three different infill materials, i.e., polyurethane (PU) foam, aluminum syntactic foam with $200\mathrm{\mu m}$ cenosphere (Al200), or plain cement concrete (PC) are explored in this investigation. Comparative studies of these composite doors are performed by varying the thicknesses and types of infill material and blast-scaled distances. The peak deflection, permanent deflection, internal energy, and total energy are compared among the doors considered herein. Weight and cost analysis of the door are also considered to determine the most cost-effective solution. The numerical results show that the polyurethane core door illustrates better performance for 150 mm of core material. However, the plain concrete infill door and cenosphere aluminum alloy core door demonstrate enhanced performance for 100 mm core thickness at 0.15, 0.2, 0.3, and $0.4\mathrm{m}/{\mathrm{kg}}^{1/3}$ scaled distances.

In this study, a new method for multihazard risk assessment of an overhead transmission line grid located over a relatively large area is proposed. The main task of this method was to assess annual risk for overhead transmission lines using failure rates estimated from historical failure data and then modifying these estimated failure rates by reanalysis data and a Bayesian updating scheme. For this purpose, a comprehensive database of structural failures was collected for climatic, geologic, and man-made hazards to overhead transmission lines. Risk denotes the probability of exceeding the sum of direct losses due to repair costs of overhead transmission line components and indirect losses due to energy not supplied or unplanned power-off after outages. The performance of the proposed method was evaluated in a case study of a target area. The results showed that climatic hazards contributed the most to total losses in the study area. Furthermore, when using indirect losses to estimate the annual loss, the average annual loss may increase by up to about 45% relative to direct losses, depending on the type of hazard. This demonstrates the importance of investigating indirect losses from structural failures in the risk assessment of overhead transmission lines.

The intelligent management of infrastructure is crucial to maintain structural stability. Although much more attention has been paid to the development of intelligent systems, the cooperation and efficiency of different components in the system is still a remaining problem. Along this line, an optimized intelligent information system (IIS) driven by big-data technology is proposed for the smart management of infrastructure. First, the integration framework of IIS is proposed, including data collection and storage, data analysis, and data expression modules. According to this framework, the remaining problems existing in each component are pointed out and the corresponding optimization measures are given. Especially for the processing technology of massive monitoring data, some trained machine learning algorithms are introduced for data analysis. As a case study, the devised system was adopted in an underwater shield tunnel for real-time monitoring, big data analysis, and information visualization. The intelligent system and advanced data-driven models presented in this study are suitable for most infrastructures, affording useful smart management experience and data processing technology for other underground projects and promoting the intelligent development of civil engineering.

In recent years, increases in terrorist attacks or other unexpected explosions have shown that their effects can cause critical damages to buildings and cause partial or total collapse of buildings. This chain collapse mechanism is named a progressive collapse. Subsoil effects on a building are often ignored in studies on progressive collapse. In this study, the effect of soil–structure interaction on the risk of progressive collapse of a steel frame is investigated by using the modified Vlasov model, which has not been used in the studies on progressive collapse before. For this purpose, a steel building model was investigated. A linear static procedure (LSP) and nonlinear dynamic procedure (NDP) with alternative path method were used for the analysis of the frame. SAP2000 software was used to model subsoil effect on the frame via an interface coded in MATLAB interactively. The results from the analyses showed that the soil–structure interaction effect can significantly affect the progressive collapse resistance of the building.

A new mortise-and-tenon joint for a fabricated rectangular tunnel is designed in this study. To explore the damage evolution of a fabricated rectangular tunnel with a mortise-and-tenon joint under internal explosion, a refined numerical model is established by using Abaqus finite element software. The sensitivity of the explosion resistance of a rectangular tunnel to different trinitrotoluene (TNT) equivalents, concrete strength grades, scaled distances, and types of joints is analyzed. The spatiotemporal effect and damage characteristics of the fabricated rectangular tunnel after a central explosion are discussed. Finally, the deformation characteristics of the tunnel corner are quantified by defining a tunnel deformation angle. The results show that the tunnel is more sensitive to the explosion equivalent and less sensitive to the concrete strength grade, and the tunnel roof is highly sensitive to TNT at different distances. The damage of the fabricated rectangular tunnel under an explosion wave has significant spatiotemporal evolution characteristics. The roof and floor of the tunnel are first impacted by explosion, and their reflection on the explosion wave will significantly enhance the explosion effect. The joint of the prefabricated tunnel has small stiffness and large flexibility, and the reflection enhancement effect on the shock wave is weak. The impact of explosive waves results in tensile stresses on the outside of the roof and floor of the tunnel and on the connection of the midpartition, where the explosion resistance is weak. The tunnel deformation angle is used to quantify the deformation damage characteristics of the tunnel. The results show that the safety height of the vehicle in the tunnel should be controlled within 3.8 m. The current research provides some valuable information for the future antiexplosion design and evaluation of the prefabricated frame tunnel.

To quickly obtain bridge surface crack information to assist bridge condition assessment or maintenance, computer vision technology is used in this paper to quantitatively identify bridge surface cracks. The features of the bridge surface in different parts of the bridge may be quite different. To carry out crack identification research in a targeted manner, first, a classification model is trained based on deep learning to discriminate the surface pattern type of bridges. Then, for the images of bridge surfaces with different features, classification and segmentation models are used to identify the location and profile of the crack in the image; in other words, the existence of the crack is first judged by the trained classification models and pixel-level segmentation is performed by the trained segmentation models on the local region where the crack exists to determine the profile of the crack. Finally, based on the trained models of three types, crack identification is realized in combination with the improved sliding window in high-resolution bridge surface images with multiclass features. According to the identification results, the unconnected cracks are separated by the connected domain. From the results, a general algorithm for calculating the length and width of each crack is established, realizing the quantification of crack identification results regardless of the surface crack form.

Building equipment failures can have a significant impact on facility operations and performance. Accurately estimating this risk of failure is a crucial aspect of maintenance decision-making for facility managers. However, developing a reliable failure model typically requires a substantial data set of past failures, which is often unavailable or not applicable for components operating in situ. This paper presents a novel machine learning method to estimate equipment failure probabilities based on more easily obtained condition inspection observations rather than relying on limited failure event data sets. The method starts with a neural network to develop an accurate degradation model from equipment inspection data. Next, the model generates estimated failure ages from known equipment condition states, thus alleviating the reliance on observed equipment failures. This paper proposes a new failure likelihood function, which is used to select parameters to characterize the failure process. The resulting parametric model can estimate failure probabilities for a given component based on time in service. This method is validated against various types of building equipment, and a case study demonstrates the use of the model to plan repair operations under failure risk uncertainty.

The striking tiled domes of the Ballet School of the Instituto Superior de Arte (ISA) in Havana are internationally acclaimed Cuban architectural icons. However, due to their deceptive tiled appearance and their only recently rediscovered hybrid tile–concrete constructive system, the domes have eluded the structural study and characterization which increasingly is demanded not only because of their unique morphology and monumental status, but also because of their visible damage. This work defined a suitable numerical analysis method for diagnosing these hybrid structures by applying both linear elastic and nonlinear, using the concrete damaged plasticity constitutive model, numerical analyses. A site investigation campaign provided materials and geometric data that informed the model creation and damage documentation that validated the analyses results. A new synthesis of architectural and technical sources covering the development and spread of the hybrid structural system allowed the ISA domes to be placed, for the first time, in their proper structural context and, consequently, for their conception and design to be understood more fully. The analyses, construction records, and site tests indicated a rebar placement error as the primary structural damage cause, and the contextual analysis informed a discussion of its possible sources, both technical and managerial. Because the investigation indicated significant risk of irreversible damage or failure without timely intervention, preliminary intervention explorations were conducted and, based on the holistic understanding of the structures, the technically, architecturally, and culturally optimal solution was identified. The study concluded that the homogeneous solids model, coupled with the concrete damaged plasticity mechanical model, yields realistic and insightful results illuminating the structural behavior, efficiency, condition or status, and collapse mode of the domes, providing necessary insight for the preservation planning for the Ballet School domes, which also will inform the study and understanding of other structures of similar composition and significance.

Abstract

Construction nonconformity assessment of buildings is critical to ensure the anticipated quality and living safety for their future occupants. Previous studies have paid less attention to identifying and analyzing building construction nonconformities (BCNs) in the design and construction (D&C) phases. They considered expert judgments in nonconformity assessment, which are critiqued for human bias, uncertainty, and imprecision. In a BCN assessment, previous studies also did not consider the specific time frame to detect construction nonconformities. This study aims to prioritize nonconformities in the D&C phases, addressing the limitations of expert judgment by applying the fuzzy group decision-making approach (FGDMA). The FGDMA computes the defuzzified scores of the nonconformities to prioritize and identify critical nonconformities. The defuzzified scores are explained further by associating them with the corresponding fuzzy numbers to address the limitations involved in expert judgments. The study also identifies the detection time of BCNs and analyzes 15 different Bangladeshi project scenarios to understand their context better. The critical nonconformities identified include premature stressing on concrete, inaccurate water-cement ratios, insufficient concrete compaction, lack of full-time site supervision, and the absence of stirrups in beam-column joints. Critical nonconformities are mostly identified during construction, and residential, commercial, and multipurpose buildings, regardless of ownership (i.e., public or private) and size, have experienced poor quality construction. This study will assist major stakeholders (owner, contractor, consultant, and regulatory authorities) to fully understand the critical nonconformities in different building projects from their preconstruction to construction phases for better quality assurance in providing a safe living and working environment for their future occupants.

Practical Applications

The study identifies critical nonconformities and their frequency, severity, and detection times in different construction projects, including residential, commercial, and multipurpose buildings and mosques. It also studies 15 different project scenarios for analyzing the nonconformities of government and privately funded/owned buildings. The most common nonconformities are premature stressing on concrete (loading to concrete members before gaining their design strength), inaccurate water-cement ratios, insufficient concrete compaction, and the absence of stirrups in beam-column joints. These nonconformities all occur due to lack of full-time site supervision and poor workmanship during construction. The dominating detection time for identifying the critical nonconformities is “during construction.” Thus, it is possible to control many by careful supervision and improved workmanship during construction. The project scenario analysis shows that residential, commercial, and multipurpose buildings, regardless of ownership (i.e., public or private), experience poor quality construction. These findings will assist stakeholders with different engagement levels in managing their roles in building projects to deliver a better quality of construction, and hence a sustainable and safe living and working environment.

Final-state field load testing is an effective method for understanding the real behavior of a bridge before it opens to traffic. The paper presents the procedure and the results of final-state static field tests on the new long span Kömürhan cable-stayed bridge with a single pylon and back span anchorage block under torsional and bending loadings. A total of six static loading cases were conducted to investigate real structural behavior of the bridge having a back span anchorage block and main span deck connected to the pylon with 42 tensioned cables in the deck center. Measured results have been obtained from the surveys and sensors while theoretical results have been calculated from the three-dimensional finite element model of the bridge final-state geometry. The results of final-state static load tests include the main span deck deflections, pylon horizontal displacements, the strains of the main span deck and the pylon, and the cable forces. A good agreement has been achieved between the experimental and theoretical results. Both experimental and theoretical results have shown that the real bridge behavior is in the elastic state under the planned test loads and supplies the design requirements. It is also observed that the results from the torsional and bending load cases positioned in the same sections are close to each other.

Long-span and wide railway bridges are loaded with various loading cases, and the structural dynamic amplification factor (DAF) corresponding to different loading cases has great variability. This study presents an approach to obtaining DAF under different loading cases. Then the approach is applied to the analyses of DAFs from long-term monitoring data. The usual values of vibration amplitude and DAF under eight single-train loading cases during long-term operation are analyzed, and the midspan amplitude and displacement DAF of the main girder under the typical two-vehicle intersection, three-train intersection, and four-train intersection loading cases are also investigated. The correlation between the DAF of the eight loading cases, the structural temperature, and the train speed under the single-train loading case is analyzed. The obtained results show that in the long-term operation, the vibration amplitude level and DAF level of the railway bridge are mainly related to the train loading case, and in 16-carriage loading cases, the DAF value is higher than that in eight-carriage loading cases in the same line. The vertical dynamic response of the midspan main girder under each working condition is small. Furthermore, depending on this evolution law, different traffic loading cases must be considered during the assessment of dynamic performance.

The ongoing challenge is to find an effective and precise fire detection method for safety concerns of utility tunnels to predict the fire danger zone and take measures for firefighting and intervention promptly. A data-driven danger zone estimation method was established based on Bayesian inference. The probability distribution of the fire danger zone can be obtained by this method. In particular, the governing equation is a simplified physical model, in which only crucial parameters of the fire states are referred to, including the fire source location, the maximum temperature, and the attenuation coefficient. This task shows superiority because it can avoid additional workload to provide the forward database for Bayesian inference. A prototype experiment was conducted in the largest utility tunnel experimental platform in China to verify the validity of the proposed method. Results demonstrated that the fire danger zone could be estimated with high accuracy merely based on several sensor data, which are not limited to specific scenes. The temperature distribution of the whole tunnel could also be predicted according to the fire parameters. Moreover, analysis of the measurement noise and disturbance conditions shows the robustness of the proposed method. Low costs, low consumption, and universality make it have broad engineering application prospects.

Facades are exposed daily to the incidence of degradation agents such as driving rain and solar radiation. Degradation mechanisms that alter the chemical or physical structure of the material are affected according to the level of incidence of such agents, thus leading to the rise of anomalies such as ceramic tile detachment and cracks. Currently, computer simulations are used to model the action of the climate agents, however, it is not always possible to estimate all agents and factors influencing the degradation process. The combined analysis of the results of the hygrothermal simulation and the degradation indicators of the ceramic facades allows a better understanding of the degradation process and mechanisms. The objective of this research is to simulate the degradation caused by the main climate agents and obtain a statistical model able to describe degradation in ceramic facades. The degradation measurement method (DMM) quantifies the degradation of facades as a function of the affected area. The degradation trend is expressed by indicators that consider the severity of different anomalies according to the affected area. The multiple linear regression model age-weighted indicated the total radiation and thermal stress and humidifying and drying cycles as the most representative variables in degradation modeling. Ceramic facades show slight degradation up to 17 years old, and the end of service life after 25 years. From 45 years of age onwards, facades tend to be in a state of severe degradation, impairing the building performance and safety of users.

Longitudinal (P) waves have been widely employed for estimating concrete compressive strength based on correlation models between wave velocity and strength. However, P-wave based methods show high uncertainty because of the challenge of identifying the weak first arrival of P waves and the sensitivity of P waves to air voids and moisture. Shear-horizontal (SH) waves are superior to P waves because of their velocity stability in concrete at various air void and moisture levels. Moreover, SH waves exhibit a high signal-to-noise ratio without mode conversion after reflection and refraction. In this study, an SH-wave based method was developed to measure the shear-wave velocity ($V_s$) of concrete directly using SH-wave transducers and then establish a correlation between $V_s$ and compressive strength. The proposed method was verified using concrete specimens produced with four water-to-cement ratios and different strength grades. The results indicate that the proposed method can significantly improve the accuracy of first arrival identification and enhance the preciseness of strength evaluation.