Infrastructures, Volume 4, Issue 3 (September 2019) – 20 articles

The purpose of this joint contribution is to study the maximum dynamic load concrete dams can withstand. The so-called “dynamic capacity functions” for these infrastructures seems now technically and commercially feasible thanks to the modern finite element techniques, hardware capabilities, and positive experiences collected so far. The key topics faced during the dynamic assessment of dams are also discussed using different point of view and examples, which include: the selection of dynamic parameters, the progressive level of detail for the numerical simulations, the implementation of nonlinear behaviors, and the concept of the service and collapse limit states. The approaches adopted by local institutions and engineers on the subject of dam capacity functions are discussed using the authors’ experiences, and an overview of time and resources is outlined to help decision makers. Three different concrete dam types (i.e., gravity, buttress, and arch) are used as case studies with different complexities. Finally, the paper is wrapped up with a list of suggestions for analysts, the procedure limitations, and future research needs. Full article

One of the most rapidly emerging measures in infrastructure asset management is Structural Health Monitoring (SHM), which aims at reducing uncertainty in structural performance by using monitoring equipment. As earthen flood defence structures typically have large strength uncertainties, such techniques can be particularly promising. However, insight in the key characteristics for successful SHM for flood defences is lacking, which hampers the practical implementation. In this study, we explore the benefits of pore pressure monitoring, one of the most promising SHM techniques for earthen flood defences. The approach is based on a Bayesian pre-posterior analysis, and results are evaluated based on the Value of Information (VoI) obtained from different monitoring strategies. We specifically investigate the effect on long-term reinforcement decisions. The results show that, next to the relative magnitude of reducible uncertainty, the combination of the probability of having a useful observation and the duration of a SHM effort determine the VoI. As it is likely that increasing loads due to climate change will result in more frequent future reinforcements, the influence of scenarios of different rates of increase in future loads is also investigated. It was found that, in all considered possible scenarios, monitoring yields a positive Value of Information, hence it is an economically efficient measure for flood defence asset management both now and in the future. Full article

The alkali–silica reaction is a chemical phenomenon that, by inducing expansion and the formation of cracks in concrete, can have a severe impact on the safety and functioning of existing concrete dams. Starting from a phenomenological two-phase isotropic damage model describing the degradation of concrete, the effects of alkali-silica reaction in an existing concrete gravity dam are evaluated and compared with real monitoring data. Considering the real temperature and humidity variations, the influence of both temperature and humidity are considered through two uncoupled diffusion analyses: a heat diffusion analysis and a moisture diffusion analysis. The numerical analyses performed with the two-phase damage model allow for prediction of the structural behaviour, both in terms of reaction extent and increase of crest displacements. The crest displacements are compared with the real monitoring data, where reasonably good agreement is obtained. Full article

Frequent terrorist activities, the use of vehicle bomb blasts and improvised explosive devices (IEDs) have brought forth the task of protection against blasts as a priority issue for engineers. Terrorists mostly target the areas where human and economic losses are significantly higher. It is really challenging to study the effects of blast loading on structures due to numerous variables. For instance, the type of detonation charge, explosive material, placement of charge and standoff distance, etc., are a few of the variables which make the system more complicated. Reinforced cement concrete (RCC) wall panels are commonly used for protecting important installations and buildings. In this research, the response of RCC wall panels has been investigated due to the blast effect caused by two TNT charge weights of 50 kg and 100 kg. These two charge weights have been selected after a detailed study of terrorist activities in the recent past. For this purpose, an existing arrangement at an important military installation, i.e., NESCOM Hospital Islamabad in Pakistan, has been selected. To reduce computational efforts, three RCC wall panels, placed side by side producing a continuous front along with a corresponding boundary and structural wall, have been considered. RCC wall panels are placed at a distance of 3 ft from the perimeter of the boundary wall and 23 ft from the structural wall. The displacement on the front face of RCC wall panels and the structural wall is measured at three levels of top, middle and bottom. ANSYS AUTODYN software has been used to simulate the model. Analysis has been carried out to identify and study the weakness of existing arrangements. Literature was reviewed for suggesting an appropriate strengthening technique for existing structures against blast loading. It was found that in addition to existing strengthening techniques, use of steel strips is amongst the most feasible technique for strengthening existing structures. It not only significantly enhanced the blast performance of structures, but it also significantly reduced z-direction displacements and pressures. The results show that the use of steel strips as the improvement technique for already placed RCC wall panels can be effective against a blast loading of up to 100 kg TNT. Full article

The dynamic modulus of hot mix asphalt (HMA) is a fundamental material property that defines the stress-strain relationship based on viscoelastic principles and is a function of HMA properties, loading rate, and temperature. Because of the large number of efficacious predictors (factors) and their nonlinear interrelationships, developing predictive models for dynamic modulus can be a challenging task. In this research, results obtained from a series of laboratory tests including mixture dynamic modulus, aggregate gradation, dynamic shear rheometer (on asphalt binder), and mixture volumetric are used to create a database. The created database is used to develop a model for estimating the dynamic modulus. First, the highly correlated predictor variables are detected, then Principal Component Analysis (PCA) is used to first reduce the problem dimensionality, then to produce a set of orthogonal pseudo-inputs from which two separate predictive models were developed using linear regression analysis and Artificial Neural Networks (ANN). These models are compared to existing predictive models using both statistical analysis and Receiver Operating Characteristic (ROC) Analysis. Empirically-based predictive models can behave differently outside of the convex hull of their input variables space, and it is very risky to use them outside of their input space, so this is not common practice of design engineers. To prevent extrapolation, an input hyper-space is added as a constraint to the model. To demonstrate an application of the proposed framework, it was used to solve design-based optimization problems, in two of which optimal and inverse design are presented and solved using a mean-variance mapping optimization algorithm. The design parameters satisfy the current design specifications of asphalt pavement and can be used as a first step in solving real-life design problems. Full article

Intelligent infrastructure, including smart cities and intelligent buildings, must learn and adapt to the variable needs and requirements of users, owners and operators in order to be future proof and to provide a return on investment based on Operational Expenditure (OPEX) and Capital Expenditure (CAPEX). To address this challenge, this article presents a biological algorithm based on neural networks and deep reinforcement learning that enables infrastructure to be intelligent by making predictions about its different variables. In addition, the proposed method makes decisions based on real time data. Intelligent infrastructure must be able to proactively monitor, protect and repair itself: this includes independent components and assets working the same way any autonomous biological organisms would. Neurons of artificial neural networks are associated with a prediction or decision layer based on a deep reinforcement learning algorithm that takes into consideration all of its previous learning. The proposed method was validated against an intelligent infrastructure dataset with outstanding results: the intelligent infrastructure was able to learn, predict and adapt to its variables, and components could make relevant decisions autonomously, emulating a living biological organism in which data flow exhaustively. Full article

Uncertainty in ground datasets often stems from spatial variability of soil parameters and changing groundwater regimes. In urban settings and where engineering ground interventions need to have minimum and well-anticipated ground movements, uncertainty in ground data leads to uncertain analysis, with substantial unwelcomed economical and safety implications. A probabilistic random set finite element modelling (RSFEM) approach is used to revisit the stability and serviceability of a 27 m deep submerged soil nailed excavation built into a cemented soil profile, using a variable water level and soil shear strength. Variation of a suite of index parameters, including mobilized working loads and moments in facing and soil inclusion elements, as well as stability and serviceability of facing and the integrated support system, are derived and contrasted with field monitoring data and deterministic FE modelling outputs. The validated model is then deployed to test the viability of using independent hydraulic actions as stochastic variables as an alternative to dependent hydraulic actions and soil shear strength. The achieved results suggest that utilizing cohesion as a stochastic variable alongside the water level predicts system uncertainty reasonably well for both actions and material response; substituting the hydraulic gradient produces a conservative probability range for the action response only. Full article

The limitations of the standard two-year interval for the visual inspection of bridges required by the U.S. National Bridge Inspection Standards have been well documented, and alternative approaches to bridge inspection planning have been presented in recent literature. This paper explores a different strategy for determining the interval between inspections and the type of inspection technique to use for bridges. The foundational premise of the proposed approach is that bridge inspections are conducted to increase knowledge about the bridge’s current condition, and therefore, are only required when uncertainty about the knowledge of the bridge condition is too high. An example case of a reinforced concrete bridge deck was used to demonstrate how this approach would work. The method utilized deterioration models for predicting corrosion and crack initiation time, considering the uncertainty in the models’ parameters. Bridge inspections were used to update the current condition information and model parameters through Bayesian updating. As this paper presents a new idea for inspection planning, not all of the data or models necessary to fully develop and validate the approach currently exist. Nonetheless, the method was applied to a simulated example which demonstrates how the timing and means of bridge inspection can be tailored to provide the required data about individual bridges needed for effective bridge management decision making. Full article

The new concept of Connected and Automated Vehicles (CAVs) necessitates a need to review the approach of managing the existing civil infrastructure system (highways, bridges, sign structures, etc.). This paper provides a basic introduction to the CAV concept, assesses the infrastructure requirements for CAVs, and identifies the appropriateness of the existing infrastructure, and needs, in terms of the condition assessment and deterioration modeling. With focus on the Vehicle-to-Infrastructure (V2I) requirements for CAVs, the main elements required on the infrastructure are the Roadside Units (RSUs), which are primarily for communication; they are similar to non-structural transportation assets, such as traffic signals, signs, etc. The ongoing pertinent efforts of agencies and the private industry are reviewed, including the V2I Deployment Coalition (American Association of State Transportation Officials (AASHTO), the Institute of Transportation Engineers (ITE), and the Intelligent Transportation Society of America (ITS America)). Current methods of transportation asset management, particularly, of non-structural elements, are also reviewed. Two reliability-based models were developed and demonstrated for the deterioration of RSUs, including the age replacement model, and a combined survivor function considering the vulnerability of the CAV elements to natural hazards, such as the hurricanes. The paper also discusses the implications of the CAV technology on traffic models, particularly, how it affects user costs’ computations. Full article

According to the latest estimates, 40% of urban areas are covered by pavements. One of the most remarkable effects on the urban environment is the increase in impermeable surfaces which leads to problems related to water infiltration into the ground and the increase in wash-off volumes. The use of permeable and porous layers in urban applications for cycle lanes, footpaths and parking areas is growing in interest, increasing the potential for control and management of urban runoff. In this paper, a physical and mechanical characterization is proposed of an innovative mixture, prepared with a polymeric transparent binder for semi-porous layers with reduced contribution to the urban heat island effect. Two versions of this mixture are compared, one with just virgin and the one with artificial synthetic aggregates, produced through the alkali-activation of waste basalt powder. Results show suitable properties for both materials if compared to porous asphalt concretes in traditional pavements. Furthermore, the application of synthetic aggregates seems to be a viable solution for the production of innovative and eco-friendly mixtures, allowing the recycling of waste materials. Full article

Isolated subsurface structures readily collect solvents spilled onto surrounding surfaces or poured into opening(s) in the manhole cover. Fatal overexposures and fires/explosions have occurred following these events. This work documents evaporation of 10 mL of lacquer thinner from a paper towel positioned near the base of a vertically oriented precast concrete chamber (volume = 2.5 m³) and exchange through opening(s) in the manhole cover monitored using a Photoionization Device (PID) sensor. A sixth order polynomial fitted by Microsoft Excel best describes the process of evaporation and dispersion in the airspace and exchange with the external atmosphere. Restoration of the uncontaminated atmosphere can require 48 hours or more under these conditions. A manhole cover containing a single opening is most likely to retain vapor for the longest period, and one with two circumferential openings opposite each other is least likely. Results presented here argue for the involvement of individuals made knowledgeable by education, experience, and training in confined spaces to address this unrecognized and underappreciated risk. Optimizing ventilation induced by natural forces in isolated subsurface structures is a natural application of the NIOSH (National Institute for Occupational Safety and Health) Prevention through Design initiative. Full article

The railway structures need constant monitoring and maintenance to ensure the train circulation safety. Quality information concerning the infrastructure geometry, namely the three-dimensional linear elements, are crucial for that processes. Along with this work, a method to automated extract three-dimensional linear elements from point clouds collected by terrestrial mobile LiDAR systems along railways is presented. The proposed method takes advantage of the stored cloud point’s attributes as an alternative to complex geometric methods applied over the point’s cloud coordinates. Based on the assumption that the linear elements to extract are roughly parallel to the rail tracks and therefore to the system trajectory, the stored scan angle value was used to restrict the number of cloud points that represents the linear elements. A simple algorithm is then applied to that restricted number of points to get the three-dimensional polylines geometry. The obtained values of completeness, correctness and quality, validate the use of the methodology for linear elements extraction from mobile LiDAR data gathered along railway environments. Full article

Ultra high-performance concrete (UHPC) application, to enhance the mechanical strength of axially loaded reinforced concrete bridge substructure elements, was proposed and investigated in an earlier study. The results recommended that depending on the UHPC shell thickness, this method may cause shifting of the critical section to undesired locations, due to over-strengthening of the repaired section, and this should be a design consideration. This paper proposes a new simplified analytical approach to calculate the bending moment capacity of the repaired circular section. This method relies on hand calculations and only requires basic material properties (compressive and tensile strengths). The results from the simplified approach are validated with a well-established numerical sectional analysis method. The proposed approach may be considered simple and more straightforward for professional engineers. Full article

Different numerical models have been proposed for seismic analysis of concrete dams by taking into account the nonlinear behavior of concrete and joints; interaction between the dam, foundation, and reservoir; and other seismic hazard considerations. Less focus, however, has been placed on the real seismic performance of the dams and their relative correlation. This paper investigates the linear and nonlinear seismic performance of two similar high arch dams with relatively different response mechanisms. The response correlation is performed from statistical and probabilistic points of view. Similarities and differences are highlighted, and the best practice to compare the responses in a class of dams is presented. It is found that some demand parameters and seismic intensity measures can reduce the dispersion of the results and increase the correlation. In general, the dam geometry has a direct relation with the deformation and spatial distribution of potential damaged area. However, it is not related to the localized damage at the most critical location. Moreover, the real crack pattern (from nonlinear analysis) is more discrete compared to the continuous overstressed/overstrained regions (from linear analysis). Full article

Transportation agencies are currently challenged to keep up with culvert infrastructure that is rapidly deteriorating due to lack of adequate maintenance and capital improvement. It is imperative for the transportation agencies to identify and rehabilitate deteriorated culverts prior to their failures. Among several concerns, lack of rational rehabilitation prioritization tools is foremost. Complicating this need further, current practices vary widely across the state departments of transportation (DOTs) which makes it difficult to develop a universal approach for prioritizing failing culverts. This paper presents and demonstrates a failure risk-based culvert prioritization approach that is compliant with the inspection procedures of the South Carolina DOT. The approach presented in this paper is specifically developed for reinforced concrete pipe (RCP) and corrugated metal pipe (CMP) materials because of their wide popularity. Outcomes from a survey of state DOTs informed the development of parametric weightings using the principles of analytical hierarchy process (AHP). Weightings developed for several critical inspection parameters are combined with the corresponding condition assessment scores to determine the failure criticality of culverts, which are subsequently combined with estimated failure consequences to determine failure risk estimates. The prioritization approach is demonstrated using the condition assessment scores of over 5200 culvert structures in South Carolina. Full article

The bottom flanges of steel plate girder bridges can be considered fracture-critical elements depending on the number of girders and bridge configuration. For such cases, it is required that inspection of these bridges be carried out using costly “arms-length” approach. New techniques in structural health monitoring (SHM) that use non-contact sensors and self-powered wireless sensors present alternative approach for inspection. Application of such techniques would allow timely detection and application of repair and strengthening, in other word, providing for more resilient bridges. This paper investigates the feasibility of using a handful of self-powered wireless or non-contact sensors for continuous or periodic monitoring and detection of fracture in steel plate girder bridges. To validate this concept, vibration measurements were performed on an actual bridge in the field, and detailed finite element analyses were carried out on a multi-girder bridge. The records obtained show that vibration amplitude was significantly increased for fractured girder, and a distinct pattern of strain variation was registered in the vicinity of fracture, all of which can be detected effectively with relevant sensors. Moreover, the amplitude and frequency of the vibration was shown to be significant enough for providing the required power for typical sensor(s). Full article

A trend towards performance-based contracting (PBC) can be observed in public infrastructure maintenance. PBC is an approach of tying the contractor’s payment to specified performance. We investigated PBC for the maintenance of highways and roads in the Netherlands, identifying issues ultimately resulting in poor contractor performance. The PBC-induced risks for clients relate to the problematic translation and measurement of specifications, the ineffectiveness of incentives, the avoidance of contractors taking full responsibility, and contract management issues. Clients should recognize the actual balance of power in the relationship with their (main) contractors, and take appropriate measures. Full article

With many lives lost every year in crashes, highway traffic safety is a major concern. With 93% of crashes being contributed to by roadway users’ poor behaviors, one of the most effective ways to improve highway traffic safety is to improve the performance of organizations enforcing traffic laws to change those poor behaviors. This research introduces a framework that makes it possible to benchmark the efficiency performance within the highway patrol. Data envelopment analysis (DEA), a mathematical methodology based on the concepts of optimization and linear programming, was used to develop that framework to measure the efficiency performance of a highway patrol’s divisions. Such framework is used to measure and compare the efficiency performance of 17 divisions of the Wyoming Highway Patrol to allow internal benchmarking and thus to improve the overall organizational performance. The concepts discussed in this paper can be implemented by highway patrol agencies for internal and external efficiency benchmarking. Although DEA has been utilized for organizational performance evaluation in multiple sectors, literature review to date has not identified any study that has specifically utilized DEA in the context of highway traffic safety from the highway patrol’s perspective. As such, this study is original and timely. Full article

Pedestrian crossing speed and waiting time are critical parameters for designing traffic signals and ensuring pedestrian safety. This study aimed to carry out microscopic level research on pedestrian crossing speed and waiting time at intersections in Dhaka. To fulfill this aim, crossing-related data of 560 pedestrians were collected from three intersections in Dhaka using a videography survey method. Descriptive and statistical analyses were carried out, and then two multiple linear regression (MLR) models were developed for these two parameters by using the collected data. From the results, 1.15 m/s was found to be the design pedestrian crossing speed. Results also show that the crossing speed of pedestrians was associated with intersection control type, gender, age, crossing type, crossing group size, compliance behavior with control direction, and crossing location. In case of waiting time, findings show that pedestrians did not want to wait more than 20–30 s to cross the road. Furthermore, the waiting time of the pedestrians varied with intersection control type, gender, age, minimum gap, waiting location, and vehicle flow. Findings of this study will help to alleviate traffic safety problems by designing an effective intersection control system. Full article

Changing climate conditions affect mining operations all over the world, but so far, the mining sector has focused primarily on mitigation actions. Nowadays, there exists increasing recognition of the need for planned adaptation actions. To this end, the development of a practical tool for the assessment of climate change-related risks to support the mining community is deemed necessary. In this study, a comprehensive framework is proposed for climate change multi-risk assessment at the local level customized for the needs of the mining industry. The framework estimates the climate change risks in economic terms by modeling the main activities that a mining company performs, in a probabilistic model, using Bayes’ theorem. The model permits incorporating inherent uncertainty via fuzzy logic and is implemented in two versatile ways: as a discrete Bayesian network or as a conditional linear Gaussian network. This innovative quantitative methodology produces probabilistic outcomes in monetary values estimated either as percentage of annual loss revenue or net loss/gains value. Finally, the proposed framework is the first multi-risk methodology in the mining context that considers all the relevant hazards caused by climate change extreme weather events, which offers a tool for selecting the most cost-effective action among various adaptation strategies. Full article