Infrastructures, Volume 6, Issue 2 (February 2021) – 19 articles

Generally, during maintenance operations on bridges and motorway viaducts, the circulation of vehicles is limited or suspended. This causes significant economic losses due to the increase in the costs of transport: delays, increased fuel consumption, higher emissions of pollutants into the atmosphere, increased risk of accidents, etc. However, few studies have analyzed the influence of bridge vibrations on the final mechanical properties of the cement mortar placing during ordinary bridge service. As such, interest is increasing in repair techniques that could achieve high structural performance without reducing road service levels. This paper provides the results obtained through an innovative laboratory trials campaign that evaluated the influence of vibrations on the mechanical properties of high-performance mortar used for repairing bridge decks. The results of 24 cubic and prismatic specimens showed the relationship between the traffic-induced vibrations and the mechanical characteristics of the studied mortar. The findings can be considered as the first methodologic step that is necessary to address further field studies, drawing a detailed link between the repair techniques and transportation user costs. Based on the obtained results, a synthetic bridge management system framework was developed that merges the road function into the structural issue with the goals of increasing the resilience of road networks and optimizing the maintenance resources budget. Full article

The ability to detect early signs of failure in buried pipe infrastructure is necessary to facilitate the continued use of ageing infrastructure for delivery of society’s essential services and move beyond disruptive and expensive reactive maintenance and repair. This paper reports detailed experiments on the use of in-pipe ultrasound techniques for assessment of ground conditions around buried plastic pipes. Two sets of ultrasonic experiment on the soil conditions are presented: (1) existence, shape, and dimension of void, and (2) water content in the soil. The ultrasound technique is shown to be capable for detecting water filled voids and assessing the soil support, critical early indicators of failure. The technique requires water as the transmission media hence is naturally suited to application in operational water distribution systems. The work represents an important advance in in-pipe condition assessment of plastic pipes, demonstrates the practical capability of the ultrasound technique, which is critical for progression towards proactive maintenance, offering cost and service improvements. Full article

The last seismic events showed that tunnel lining may suffer extensive damage. Employing numerical modeling has a great importance in predicting the seismic performance of tunnels. This paper tests the tunnel lining of the Zaouit Ait Mellal (ZAM) twin tunnels located between the cities of Marrakesh and Agadir in Morocco. Dynamic analysis was adopted by FLAC 2D software using the finite-difference elements. Four soil cross-sections were chosen, with different support devices installed along the twin tunnels, such as rock bolts and steel ribs. The seismic signals introduced as input were obtained from three different earthquakes: Al Hoceima 2004 in Morocco, EL Centro 1940 in the USA, and Kobe 1995 in Japan. The numerical results show that the deformation of the tunnel lining is more noteworthy in the sections using steel ribs compared to those using rock bolts, which is observed by the large values of relative displacement, reaching 1020 (mm) and 2.29 × 105 (N.m/m) of maximum bending moment. The analysis indicates that these sections present higher vulnerability during an earthquake, which should be considered when looking at the overall safety of the tunnel. Full article

One of the most important components of pavement management systems is predicting the deterioration of the network through performance models. The accuracy of the prediction model is important for prioritizing maintenance action. This paper describes how the accuracy of prediction models can have an effect on the decision-making process in terms of the cost of maintenance and rehabilitation activities. The process is simulating the propagation of the error between the actual and predicted values of pavement performance indicators. Different rate of error (10%, 30%, 50%, 70%, and 90%) was added into the result of prediction models. The results showed a strong correlation between the prediction models’ accuracy and the cost of maintenance and rehabilitation activities. The cost of treatment (in millions of dollars) over 20 years for five different scenarios increased from ($54.07–$92.95), ($53.89–$155.48), and ($74.41–$107.77) for asphalt, composite, and concrete pavement types, respectively. Increasing the rate of error also contributed to the prediction model, resulting in a higher benefit reduction rate. Full article

Bridge inspection standards in the United States require routine visual inspections to be conducted on most bridges at a maximum interval of two years regardless of the bridge condition. Limitations of this uniform calendar-based approach have been reported in the literature. Accordingly, the objective of this study is to provide a new systematic approach for inspection planning that integrates information from bridge condition prediction models, inspection data, and expert opinion using Bayesian analysis to enhance inspection efficiency and maintenance activities. The uncertainty-based inspection framework proposed in this study can help bridge owners avoid unnecessary or delayed inspections and repair actions, determine the inspection method, and consider more than one deterioration process or bridge component during the inspection planning process. The inspection time and method are determined based on the uncertainty and risks associated with the bridge condition. As uncertainty in the bridge condition reaches a defined threshold, an inspection is scheduled utilizing nondestructive techniques to reduce the uncertainty level. The framework is demonstrated on a new and on an existing reinforced concrete bridge deck impacted by corrosion deterioration. The results show that the framework can reduce the number of inspections by 50% compared to conventional scheduling methods, and the uncertainty regarding the bridge maintenance time is reduced by 16%. Full article

The main aim of this paper is to review recent studies over the past 10 years investigating the influencing factors for improving the mechanical properties of concrete. This focuses on concrete comprising of pozzolanic materials, partially or entirely replacing ordinary Portland cement, in the concrete mixture. Firstly, the effectiveness of main factors such as temperature, water to solid (W/S) ratio, and alkaline solution ratio was briefly discussed. Next, the effects of significant factors such as different superplasticizer and alkaline solutions and combinative materials on the improvement of concrete workability were reviewed and compared. Eventually, other concrete properties such as water absorption and durability were discussed in the last section. After reviewing all types of concrete additives, including mineral or chemical materials, the influence of these admixtures under different laboratory conditions were highlighted to objectively evaluate the benefits of each factor. As a whole, the significant reasons of such experimental tests arising from the usage of these materials, in accordance with the laboratory results obtained from these investigations, are discussed. Full article

Guaranteeing adequate safety levels in critical infrastructures such as bridges is essential to modern societies and their vital services. Bridges with reinforced concrete structures are subject to deterioration, especially due to corrosion effects. Gerber saddles are among the key components of bridges which are especially exposed to environmental actions due to their position and reduced possibility of inspection. In this paper, a framework for the durability analysis of these components is proposed, considering the simultaneous presence of permanent loads and environmental actions under the form of chloride ions. Nonlinear numerical simulations adopting the finite element code ATENA are performed, accounting for chloride ingress analyses. The presence of cracks (due to applied loads and/or design/construction defects) which may speed-up corrosion propagation, steel reinforcement loss, cracking and spalling, and their effects on the load-bearing capacity is considered. This framework has been applied to the Gerber saddles of a prominent reinforced concrete (RC) bridge, namely the Musmeci bridge in Potenza, Italy. Durability analyses made it possible to evaluate the saddles’ strength capacity (i) at the time of construction, (ii) after forty-five years since the construction, and (iii) at an extended time of fifty years. The results show that corrosion can influence both the ultimate load capacity and the collapse mechanism. Full article

The continuous growth of cities brings out various concerns for improved development and management of the multifaceted urban systems, including those of resilience and smartness. Despite the many significant efforts in the research field, both notions remain changeable, thus retaining the lack of commonly accepted conceptual and terminological frameworks. The paper’s research goals are to designate the current direct and indirect links in the conceptualizations and research trends of the resilience and smart city frameworks and to prove the potential of the conceptual convergence between them in the context of urban systems. The application of a semi-systematic literature review, including bibliometric evidence and followed by content analysis, has led to the observation that as the resilience discourse opens up to embrace other dimensions, including technology, the smart city research turns its interest to the perspective of urban protection. Therefore, both concepts share the goal for urban sustainability realized through specific capacities and processes and operationalized with the deployment of technology. The paper’s findings suggest that the conceptual and operational foundations of these two concepts could support the emergence of an integrated framework. Such a prospect acknowledges the instrumental role of the smart city approach in the pursuit of urban resilience and unfolds a new model for sustainable city management and development. Full article

The advent of modern geodetic satellite techniques (GNSS, including GPS) permitted to observe dynamic deflections of bridges, initially of long flexible ones, and more recently of short, essentially stiff bridges with modal frequencies > 1 Hz, and with small SNR (signal-to-noise ratio), even SNR < 1. This was an enormous progress, but not without problems. Apart from monitoring results consistent with structural models, experimental data and serviceability criteria, there exist some apparently unexplained cases of stiff bridges for which there have been claimed apparent dynamic deflections too large for common healthy structures. Summarizing previous experience, this article: (i) discusses structural constraints, experimental evidence, and serviceability limits of bridges as constraints to GNSS monitoring; (ii) examines a representative case of careful monitoring of a reinforced concrete road bridge with reported excessive dynamic deflections; and (iii) explains such deflections as a result of a double process generated by large reflective surfaces of passing vehicles near the antenna; first corruption/distortion of the satellite signal because of high-frequency dynamic multipath, and second, shadowing of some satellites; this last effect leads to a modified observations system and to instantaneously changed coordinates and deflections. In order to recognize and avoid such bias in GNSS monitoring, a strategy based on practical rules and structural constraints is presented. Full article

One of the most discussed aspects of vibration-based structural health monitoring (SHM) is how to link identified parameters with structural health conditions. To this aim, several damage indexes have been proposed in the relevant literature based on typical assumptions of the operational modal analysis (OMA), such as stationary excitation and unlimited vibration record. Wireless smart sensor networks based on low-power electronic components are becoming increasingly popular among SHM specialists. However, such solutions are not able to deal with long data series due to energy and computational constraints. The decentralization of processing tasks has been shown to mitigate these issues. Nevertheless, traditional damage indicators might not be suitable for onboard computations. In this paper, a robust damage index is proposed based on a damage sensitive feature computed in a decentralized fashion, suitable for smart wireless sensing solutions. The proposed method is tested on a numerical benchmark and on a real case study, namely the S101 bridge in Austria, a prestressed concrete bridge that has been artificially damaged for research purposes. The results obtained show the potential of the proposed method to monitor the conditions of civil infrastructures. Full article

The area of dynamic planning of construction sites is unexplored. Although there is a large amount of scientific interest in the literature in dynamic planning of construction site layouts, with different methodologies developed, studies on construction site relocation do not exist. The purpose of this study is to cover this gap in the literature and contribute to the body of knowledge by presenting for the first time a dynamic planning of a construction site and its importance in linear construction projects and to validate this methodology through real case studies. The decisive variables that determine the appropriate site locations and the costs that arise from these choices are analyzed. The choice that maximizes the production rate of the construction site and thus minimizes the costs is further investigated. An algorithm has also been developed that estimates the cost of transportation of the equipment used in the project and thus enables the investigation of the “ideal” location that minimizes this cost. The “ideal” site location is examined according to the time schedule of the project at time intervals that are determined by the work progress. The optimization algorithm aims to minimize the cost that derives from non-productive activities. The validity of the proposed model is tested in four motorway projects. A sensitivity analysis concerning different sequences in the construction methods reveals remarkable changes in cost fluctuations depending on project size. The outcomes show that for the second, third, and fourth projects, dynamic planning is demanded, and the profit gained ranges from 1 to 1.5% of total budget cost. Financing expenses could be covered by this profit. The case studies presented are derived from linear infrastructure projects that are more sensitive to this approach because of their size and their budget that both affect the results. Full article

The type and the properties of the soil can potentially intensify the internal forces on buildings during seismic loads. To predict the effects of the soil parameters on the soil–structure interaction of buildings, it is necessary to consider the soil–structure interaction (SSI) in the modeling process. Therefore, this document aims to evaluate the seismic effect on the maximal displacement and inter-story drift, and evaluate the behavior of buildings under the second-order effect known in the literature as the P-delta effect. For this purpose, three cases of buildings with 5, 10 and 15 stories were modelled using a FLAC 2D finite-difference element calculation software with infinite soil conditions, including five types of base with four types of soil (one cohesive soil and three non-cohesive soils) considering the soil–structure interaction and a fixed base (without soil–structure interaction). According to the results for the above-mentioned boundary, as the height of the building increases and due to the weak properties of the soil, we notice that the maximal displacements and inter-story drift increase considerably. To that purpose, we recommend considering the second-order effect in seismic design, especially for non-cohesive soil. Full article

Pavement markings are essential elements of transportation infrastructure with critical impacts on safety and mobility. They provide road users with the necessary information to adjust driving behavior or make calculated decisions about commuting. The visibility of pavement markings for drivers can be the boundary between a safe trip and a disastrous accident. Consequently, transportation agencies at the local or national levels allocate sizeable budgets to upkeep the pavement markings under their jurisdiction. Infrastructure asset management systems (IAMS) are often biased toward high-capital-cost assets such as pavements and bridges, not providing structured asset management (AM) plans for low-cost assets such as pavement markings. However, recent advances in transportation asset management (TAM) have promoted an integrated approach involving the pavement marking management system (PMMS). A PMMS brings all data items and processes under a comprehensive AM plan and enables managing pavement markings more efficiently. Pavement marking operations depend on location, conditions, and AM policies, highly diversifying the pavement marking management practices among agencies and making it difficult to create a holistic image of the system. Most of the available resources for pavement marking management focus on practices instead of strategies. Therefore, there is a lack of comprehensive guidelines and model frameworks for developing PMMS. This study utilizes the existing body of knowledge to build a guideline for developing and implementing PMMS. First, by adapting the core AM concepts to pavement marking management, a model framework for PMMS is created, and the building blocks and elements of the framework are introduced. Then, the caveats and practical points in PMMS implementation are discussed based on the US transportation agencies’ experiences and the relevant literature. This guideline is aspired to facilitate PMMS development for the agencies and pave the way for future pavement marking management tools and databases. Full article

The recycled aggregate is an alternative with great potential to replace the conventional concrete alongside with other benefits such as minimising the usage of natural resources in exploitation to produce new conventional concrete. Eventually, this will lead to reducing the construction waste, carbon footprints and energy consumption. This paper aims to study the recycled aggregate concrete compressive strength using Artificial Neural Network (ANN) which has been proven to be a powerful tool for use in predicting the mechanical properties of concrete. Three different ANN models where 1 hidden layer with 50 number of neurons, 2 hidden layers with (50 10) number of neurons and 2 hidden layers (modified activation function) with (60 3) number of neurons are constructed with the aid of Levenberg-Marquardt (LM) algorithm, trained and tested using 1030 datasets collected from related literature. The 8 input parameters such as cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age are used in training the ANN models. The number of hidden layers, number of neurons and type of algorithm affect the prediction accuracy. The predicted recycled aggregates compressive strength shows the compositions of the admixtures such as binders, water–cement ratio and blast furnace–fly ash ratio greatly affect the recycled aggregates mechanical properties. The results show that the compressive strength prediction of the recycled aggregate concrete is predictable with a very high accuracy using the proposed ANN-based model. The proposed ANN-based model can be used further for optimising the proportion of waste material and other ingredients for different targets of concrete compressive strength. Full article

The present work introduces a case study on the climate resilience of interconnected critical infrastructures to forest fires, that was performed within the framework on H2020 EU-CIRCLE project (GA 653824). It was conducted in South France, one of the most touristic European regions, and also one of the regions at the highest forest fire risk that is projected to be amplified under future climate conditions. The case study has been implemented through a co-creation framework with local stakeholders, which is critical in moving beyond physical damages to the infrastructures, introducing the elements of infrastructure business continuity and societal resilience. Future forest fires extremes are anticipated to impact the interconnections of electricity and transportation networks that could further cascade to communities throughout South France. The work highlighted the benefits of enhancing co-operation between academia, emergency responders, and infrastructure operators as a critical element in enhancing resilience through increased awareness of climate impacts, new generated knowledge on fire extremes and better cooperation between involved agencies. Full article

Drilling activity produces a significant amount of road traffic through unpaved and paved local roads. Because oil production is an important contributor to the local economy in the state of North Dakota, the state and local transportation agencies make efforts to support local energy logistics through the expansion and good repair and maintenance of transportation infrastructure. As part of this effort, it is important to build new roads and bridges, maintain existing road pavement and non-marked road surface conditions, and improve bridge and other transportation infrastructure. Therefore, the purpose of this study is to review previous oil location prediction models and propose a novel geospatial model to predict drilling locations which have a significant impact on local roads, to verify and provide a better prediction model. Then, this study proposes a SIR (susceptible–infected–recovered) epidemic model to predict oil drilling locations which are traffic generators. The simulation has been done on the historical data from 1980 to 2015. The study found that the best fit parameters of β (contact rate) and μ (recovery rate) were estimated by using a dataset of historical oil wells. The study found that the SIR epidemic model can be applied to predict the locations of oil wells. The proposed model can be used to predict other drilling locations and can assist with traffic, road conditions, and other related issues, which is a much needed predictive model that is key in transportation planning and pavement design and maintenance. Full article

A nonparametric machine learning model was used to study the behaviour of the variables of a concrete arch dam: Roode Elsberg dam. The variables used were ambient temperature, water temperatures, and water level. Water temperature was measured using twelve thermometers; six thermometers were on each flank of the dam. The thermometers were placed in pairs on different levels: avg6 (avg6-R and avg6-L) and avg5 (avg5-R and avg5-L) were on level 47.43 m, avg4 (avg4-R and avg4-L) and avg3 (avg3-R and avg3-L) were on level 43.62 m, and avg2 (avg2-R and avg2-L) and avg1 (avg1-R and avg1-L) were on level 26.23 m. Four neural networks and four random forests were cross-validated to determine their best-performing hyperparameters with the water temperature data. Quantile random forest was the best performer at mtry 7 (Number of variables randomly sampled as candidates at each split) and RMSE (Root mean square error) of 0.0015, therefore it was used for making predictions. The predictions were made using two cases of water level: recorded water level and full dam steady-state at Representative Concentration Pathway (RCP) 4.5 (hot and cold model) and RCP 8.5 (hot and cold model). Ambient temperature increased on average by 1.6 °C for the period 2012–2053 when using recorded water level; this led to increases in water temperature of 0.9 °C, 0.8 °C, and 0.4 °C for avg6-R, avg3-R, and avg1-R, respectively, for the period 2012–2053. The same average temperature increase led to average increases of 0.7 °C for avg6-R, 0.6 °C for avg3-R, and 0.3 °C for avg1-R for a full dam steady-state for the period 2012–2053. Full article

Using a face validity approach, this paper provides a validation of the Database Forensic Investigation Metamodel (DBFIM). The DBFIM was developed to solve interoperability, heterogeneity, complexity, and ambiguity in the database forensic investigation (DBFI) field, where several models were identified, collected, and reviewed to develop DBFIM. However, the developed DBFIM lacked the face validity-based approach that could ensure DBFIM’s applicability in the DBFI field. The completeness, usefulness, and logic of the developed DBFIM needed to be validated by experts. Therefore, the objective of this paper is to perform the validation of the developed DBFIM using the qualitative face validity approach. The face validity method is a common way of validating metamodels through subject expert inquiry on the domain application of the metamodel to assess whether the metamodel is reasonable and compatible based on the outcomes. For this purpose, six experts were nominated and selected to validate the developed DBFIM. From the expert review, the developed DBFIM was found to be complete, coherent, logical, scalable, interoperable, and useful for the DBFI field. Full article