Infrastructures, Volume 7, Issue 9 (September 2022) – 18 articles

The development of reliability theory has led to the setting of tasks requiring consideration of the efficiency and functional safety of technical objects of transport over the life cycle. The paper demonstrates the possibility of using the universal laws of elastic wave theory to describe natural phenomena occurring in complex dynamic systems, on the examples of solving issues arising in the interaction of rolling stock and the railway track. The accounting of the time component and the ability of elastic waves to propagate energy in time and space allowed considering any interaction process as a chain of processes, incidence-reflection-refraction of force impulses of interaction. Understanding the physics of dynamic processes that occur in objects while performing their intended functions allows developers to improve objects in such a way as to minimize their life cycle cost and maximize their ability to perform their intended functions under different operating conditions. In addition, it allows the expansion of existing methods and approaches to diagnostics of dynamic transport systems. All this is a base for making it possible to develop an innovative and effective tool for engineers and scientists to assess the impact of technosphere transport objects on human habitats. Full article

The applications of the deep belief network (DBN) for addressing practical engineering issues have recently emerged all over the world thanks to its accuracy and availability of data. In this paper, a predictive model using DBN was employed to investigate the factors that affect the ground-penetrating radar (GPR) signals from the rebar embedded in concrete structures. Four variables, namely temperature, relative humidity, chloride contamination level, and rebar surface corrosion condition were used as the model inputs for the investigation. Comprehensive data acquired from previously published documents were used to establish the proposed DBN model. It was shown that temperature and chloride contamination level variables generated significant effects on the GPR amplitude signal from rebar. In contrast, the relative humidity and rebar surface corrosion condition parameters were found to yield a minimal influence on the output of the proposed model. The DBN model can be used to predict the amplitude of GPR signals from the four inputs with a high level of accuracy. Specifically, the coefficient of determination (R²) was 0.9634 and 0.9681 for the testing dataset and the entire database, respectively. Full article

The seismic assessment of existing bridges is of the utmost importance to characterise the main structural deficiencies, estimate the risk, prioritise retrofit interventions, or estimate losses and repair costs in case of earthquakes. The above tasks require information on the damage mechanisms likely to occur as well as on the damage extent over the structure. Such types of information are generally not provided by classical fragility analysis, which is mainly focused on the evaluation of the global performance of the bridge. In this paper, a systematic probabilistic methodology for the evaluation of bridge fragility is proposed. The methodology aims at offering insight into the failure mechanisms most likely to occur and the evolution and extent of damage within the bridge structure. First, a mathematical description of the proposed analysis methods is given, then an application to a realistic case study—a reinforced concrete multi-span simply supported deck link-slab bridge—is provided to illustrate the applicability of the tool. A nonlinear 3D finite element model is developed, and a multiple-stripe (nonlinear dynamic) analysis is performed by using a stochastic bidirectional seismic input. The results highlight the suitability of the proposed methodology to reveal the main structural deficiencies, the relations among different failure mechanisms (involving piers, bearings, abutments, etc.), and the expected damage extent. Full article

Gravity retaining walls are a vital structure in the area of geotechnical engineering, and academicians in earlier studies have conveyed substantial uncertainties involved in calculating the factor of safety against overturning, using a deterministic approach. Hence, to enhance the accuracy and eliminate the uncertainties involved, artificial intelligence (AI) was used in the present research. The main aim of this study is to propose a high-performance machine learning (ML) model to determine the factor of safety (FOS) of gravity retaining walls against overturning. The projected methodology included a novel hybrid machine learning model that merged with an adaptive neuro-fuzzy inference system (ANFIS) and meta-heuristic optimization techniques (particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FFA) and grey wolf optimization (GWO)). In this research, four hybrid models, namely ANFIS-PSO, ANFIS-FFA, ANFIS-GA and ANFIS-GWO, were created to estimate the factor of safety against overturning. The proposed hybrid models were evaluated on two distinct datasets (training 70% and testing 30%) with three input combinations, namely cohesion (c), unit weight of soil (Υ) and angle of shearing resistance (φ). To access the prediction power of different hybrid models, various statistical parameters such as R², AdjR², VAF, WI, LMI, a-20 index, PI, KGE, RMSE, SI, MAE, NMBE and MBE were computed for training (TR) and testing (TS) datasets. The overall performance of the models indicated that ANFIS-PSO provided better results among all four models. The reliability index was computed using the first-order second-moment (FOSM) method for all models, and the probability of failure was also computed. A Williams plot was drawn to check the applicability domain of the hybrid model and to check the influence of different input parameters on the prediction of the factor of safety, and the Gini index was also computed. Full article

A database of 479 glass formulations used to immobilize radioactive wastes for facilities in a safe and resilient infrastructure was analyzed, searching for underlying statistical patterns and associated glass performance features. The analyzed data cover many oxides, including SiO₂, B₂O₃, Na₂O, Fe₂O₃, and some fluorides. Borosilicates were the most common glasses (60.1%), while silicates were only 11.9%. In addition to these two families, five radioactive waste vitrification matrices were identified: Boroaluminosilicates, iron phosphates, aluminosilicates, sodium iron phosphates, and boroaluminates, totaling seven glass families. Almost all compositions (97.7%) contained sodium oxide, followed by silica (91.4%), iron (82.7%), boron (73.7%), phosphorus (54.9%), and cesium oxides (26.1%). Multivariate exploratory methods were applied to analyze and classify glass compositions using hierarchical and non-hierarchical (K-means) clusters and principal component analysis. Four main clusters were observed, the largest comprising 417 formulations containing mainly silicates, borosilicates, aluminosilicates, and boroaluminosilicates; two principal components, representing 73.75% of all compositions, emerge from these four clusters derived from a covariance analysis. The principal components and four clusters may be associated with the following glass features in terms of glass compositions: liquidus temperature, glass transition temperature, density, resistivity, microhardness, and viscosity. Some general underlying properties emerged from our classification and are discussed. Full article

Alkali-activated binders (AABs) offer the opportunity to upcycle a variety of residues into products that can have added value. Although AABs are reported to have a superior high-temperature performance, their thermal behavior is heavily governed by their microstructure. The present study, therefore, evaluates the effect of varying fly ash:slag ratios, activator modulus (Ms), and high temperatures on the microstructure of AAB using X-ray diffraction, Fourier transform infrared spectroscopy, and scanning electron microscopy coupled with energy-dispersive spectroscopy. Furthermore, the mechanical properties of alkali-activated concrete (AAC) are investigated through compressive, bond, flexural, and split tensile strengths. A life cycle assessment of AAC is performed using the ReCiPe 2016 methodology. The results from microstructural experiments show the formation of new crystalline phases and decomposition of reaction products on high temperature exposure, and they correlate well with the observed mechanical performance. The 28-days compressive strength with slag content is enhanced by 151.8–339.7%. AAC with a fly ash:slag ratio of 70:30 and Ms of 1.4 is proposed as optimal from the obtained results. The results reveal that the biggest impact on climate change comes from transport (45.5–48.2%) and sodium silicate (26.7–35.6%). Full article

It is generally assumed that there is a statistically valid correlation between the length of a roadway network, in addition to other factors such as its classification and/or average travel speed, and economic indicators such as Gross Domestic Product (GDP) and the Municipal Relative Weight (MRW), considering that the roadway network and transport development generate economic development in a region. This study reports the results of correlating several variables which are economic indicators of roadway networks, both at a municipal and a departmental level, in Colombia; it was concluded that at the level of municipalities, there is no valid correlation between MRW, as a dependent variable, and the average travel speed and the sum of the length (in kilometers) of the roadways that connect villages, as independent variables. There was a correlation with neither the MRW as an independent variable nor the traveling distance and time for each municipality concerning the capital city of each respective department. Finally, it was found that the department agribusiness GDP was associated with the length of the tertiary roadway network and with the primary network, with an R2 of 0.7. This study concludes that activities in rural zones are the ones that generate the greatest impact on roadway investment within a region. Full article

This research investigated high calcium fly ash geopolymer stabilized recycled concrete aggregate (RCA-FAG) as pavement base material. The effect of recycled concrete aggregate (RCA):high calcium fly ash (FA) ratios, sodium silicate (Na₂SiO₃):sodium hydroxide (NaOH) ratio, and curing time on the unconfined compressive strength (UCS) and scanning electron microscope (SEM) properties of RCA-FAG samples were evaluated. The maximum dry unit weight of the RCA-FAG sample was 20.73 kN/m³ at RCA:FA ratio of 80:20 and Na₂SiO₃:NaOH ratio of 60:40. The 7-d UCS of RCA-FAG samples increased as the FA content and Na₂SiO₃:NaOH ratio increased. The 7-d UCS of the RCA-FAG sample was better than that of the RCA with no FA because FA particles filled in RCA particles, resulting in a dense matrix. The 7-d UCS of RCA-FAG samples passed the 7-d UCS requirement for the low-traffic road. All ingredients met the 7-d UCS requirement for the high-traffic road except the sample with RCA:FA of 100:0 and Na₂SiO₃:NaOH of 50:50 and 60:40. The 7-d SEM images indicated that spherical FA and RCA particles are bonded together, resulting in the dense matrix for all Na₂SiO₃:NaOH ratios. The proposed equation for predicting the UCS of RCA-FAG offered a good coefficient of correlation, which is useful in designing pavement base material from RCA-FAG material. Full article

The optical response of opaque materials in an urban envelope plays an important role in a city’s energy exchange with the environment as it defines the absorption of radiation and emission of heat. In the present work, the most common surfaces of the finishing materials of pavement and walls in the city of Madrid (Spain) were identified, and their reflectance was measured in situ to determine their solar absorptance and color coordinates. Most of the selected pavement showed a relatively high solar absorptance in the range of 0.87 to 0.60, while in vertical surfaces, the range was 0.85 to 0.29. The variations of the color coordinates obtained for pavement were 27.1, 11.4, and 6.7 for ΔL*, Δa*, and Δb*, respectively. Significantly higher values were obtained in the case of vertical surfaces (47.5, 20.5, and 23.6, respectively). The results were included into a database intended to be the seed for a catalogue of the experimental thermo-optical properties of opaque envelope materials in Madrid. The catalogue will be useful for the analysis of the stimuli generated by the urban environment for citizens and for achieving more reliable results from energy simulation tools in the search for strategies to improve urban comfort and sustainability. Full article

The present work is concerned with the introduction of a new first level pre- and post-earthquake seismic assessment protocol for buildings that relies on the use of recorded structural response. As earthquakes represent a constant and unpredictable threat for the building stock around the globe, the protocols already in use for assessing the risk should be revised and should also take into account the information hidden in data recorded in the field. Nowadays, data collection does not require expensive equipment and over-qualified personnel. In this direction, the proposed seismic assessment protocol aims to illustrate the ease of widely adopting Structural Health Monitoring (SHM) equipment (e.g., accelerographs), based on the work that has been carried out over the past years on subjects related to earthquake risk estimation. Building taxonomy and damage estimation, like those found in Hazus^®–MH and other hazard assessment tools, can be enriched and modified properly to distinguish and classify the very earthquake-prone buildings from the others, and tag them for further assessment and rehabilitation as seismic codes suggest. Full article

Efficient transportation and road network infrastructure plays the most significant role in the development of any region. However, the effectiveness of a road network is often affected by problems like road condition, traffic congestion, road blockage, road accidents etc. Digitization of the road network and its analysis can therefore be an effective tool towards resolving these issues. The free and open-source software Quantum Geographic Information System (QGIS) is well suited for such an analysis. QGIS can be used for construction of suitable road network maps of certain areas which provide sufficient information for such analysis. Cachar district of Assam, particularly Silchar circle, suffers frequently from traffic problems for being a network hub for other neighbouring revenue circles and some neighbouring north-eastern states. The scientific visualization and analysis of the road network of this district using GIS tools is still not available in literature. The results of the current study and their application could help the administrators and decisionmakers to build a sustainable road network. In this paper, we make an attempt to digitize the existing road network of Cachar district for its proper analysis. We compile the major and minor road density maps for the five revenue circles of the district. We use OpenStreetMap (OSM) to access and download existing road network in the district. Further, using the shortest path tool in QGIS, we find and display the shortest route between two junction points in the road network. Finding optimal route can be of great utility during emergency medical responses or fire or flood situations. Most of the major and minor roads within Cachar district were digitized in QGIS environment to perform the road network analysis. Full article

A road network is the key foundation of any nation’s critical infrastructure. Pavements represent one of the longest-living structures, having a post-construction life of 20–40 years. Currently, most attempts at maintaining and repairing these structures are performed in a reactive and traditional fashion. Recent advances in technology and research have proposed the implementation of costly measures and time-intensive techniques. This research presents a novel automated approach to develop a cognitive twin of a pavement structure by implementing advanced modelling and machine learning techniques from unmanned aerial vehicle (e.g., drone) acquired data. The research established how the twin is initially developed and subsequently capable of detecting current damage on the pavement structure. The proposed method is also compared to the traditional approach of evaluating pavement condition as well as the more advanced method of employing a specialized diagnosis vehicle. This study demonstrated an efficiency enhancement of maintaining pavement infrastructure. Full article

Mountainous roads often have to overcome considerable differences in height, which is why hairpin bends find a valid and common use. Despite this, there is a lack of specific international standards. Given the absence of a national standard governing the mountain roads’ design, in Italy, as in many other countries, the Swiss standard SNV 640198a is generally applied. This standard does not guarantee the correct geometric design of hairpin bends for Italian vehicle fleets and fleets according to the Directive 2002/7/EC. In this paper, the authors have developed a new methodology based on the Swiss standard upgrade, which is applicable internationally. Starting from hairpin bends’ geometric layouts provided by SNV 640198a and from related considerations, respectively, to the gyration formulae use and to swept path analysis’ simulations, they developed new planimetric layouts compatible with the vehicle fleet and with the cross-sectional dimensions of Italian roads. In this way, a generally valid methodology applicable to any international context was defined. In particular, the study allowed the definition of new geometric layouts to be used in hairpin bend design/retrofitting when it is necessary to guarantee the simultaneous entry into the bend of a 12 m long bus, and a car travelling in the opposite direction. Finally, the proposed methodology was applied to a mountain road case study in the Lucanian Dolomites area; an area of great tourist, cultural and environmental interest in southern Italy. Full article

The impact that construction has on sustainability as a relevant consumer of materials is well known, especially with regard to cement, which contributes to high CO₂ emissions. It is well known that in tile adhesives, cement add positive technical contributes, supporting tensile adhesion, especially after water immersion and freeze–thaw cycles. On the other hand, it is also known that that it is possible to replace Portland cement with alternative sources, such as blast furnace slag, fly and bottom ashes, or other pozzolanic materials. Even so, other materials can be also used to contribute to additional performance. This work intends to prove that using recycled materials or by-products is not just a potential way to replace existing materials, improving environmental sustainability, but also contributes additional value to mortars, such as cement-based tile adhesives. Different recycled waste materials are introduced to a cement-based tile adhesive and the evaluation of properties according to EN 12004 is conducted. The results show how the introduction of recycled rubber can contribute to improve the workability of a tile adhesive, acting as a lightweight aggregate. Moreover, it can contribute to reducing the dynamic elasticity modulus; thus, it has a potential contribution to reduce global tensions in tiling systems, and the adhesion results are maintained by the introduction of slag, another recycled material. The weight reduction reduces mortar consumption, one of the main targets to support indicated strategy and justify a more sustainable performance. The results indicate that the introduction of rubber and slag provide good technical and mechanical performance for the mortars, as well as excellent workability. Full article

The seismic response of underground structures such as subway stations is critical. However, the complex underground structure experiments under a dynamic centrifuge shaking table are significantly limited. This study conducts a shaking table test of a two-story three-span underground structure under 50 g gravitational centrifuge acceleration to investigate soil–structure interaction effects (SSI). The test is performed on a sand soil-structural model using a laminar shear box with depths of 2.5 m, and the input motion is a Parkfield wave. The experimental results indicate that the central column of the two-story three-span underground structure is the weak component during the earthquake. In addition, the numerical simulations of the soil–structure system are carried out to study the effect of buried depth and foundation soil type on the seismic response of the underground structures. The experimental and numerical results proved that the performed centrifuge test can reproduce the key seismic response characteristics of the SSI in the prototype underground structure and provided guidelines to design a similar underground structure in the future development of the urban subway systems. Full article

Infrastructure advancement is a key attribute that defines the development and effective growth of a city or region. Since the introduction of the United Nations Sustainability Development Goals (UN SDGs), more construction companies are focusing on adopting sustainable construction practices. However, a lack of relevant competencies among employees at various infrastructure construction organizations often hinders the successful implementation of sustainable practices. Education that facilitates systematic professional development and contemporary competencies’ acquisition is a key to overcoming this barrier. Thus, the current study adopts a three-stage review to identify current research trends and inform future research directions for the enhancement of the environmental sustainability competencies base for infrastructure professionals. A bibliometric assessment was first conducted followed by a focused literature review on sustainability education. Subsequently, two engineering and construction higher education curricula were assessed for infrastructure sustainability content. The results from the three-step analysis indicate that the growing interest in sustainability concepts in the construction industry is driven by policy changes. A lack of financial incentives, the unavailability of resources, a lack of motivation amongst graduates, and limited time in the infrastructure construction sector were identified as some of the major impediments for developing the environmental sustainability competencies base. The requirement for integrated and structured Continuous Professional Development (CPD) programs to facilitate ongoing knowledge acquisition and structured evaluation of professional knowledge in addition to effective undergraduate program development are highlighted. The necessity for a digitally personalised platform that can graphically represent current progress and future milestones and enable peer interaction and collaboration was also identified as critical for improving the uptake of such programs. The findings from this study could be useful for government agencies and infrastructure construction organizations keen to enhance the environmental sustainability knowledge of their employees. Future studies are required to assess sustainability education across the globe and to develop new learning components of infrastructure sustainability that are validated through stakeholder participation. Full article

For too long, many refined transportation models have focused solely on private and public transportation, assuming that bicycles only require simple models, such as bird flight distance or trips on horizontal tracks at a constant speed. This paper aims to study the impact of the road characteristics, such as road gradient, type of road and pavement surface of the road, on cyclists’ behavior using dedicated modules of MATSim. For that, we compare two approaches: a standard approach which does not consider the road characteristics, and a second approach that uses MATSim bicycle extension of Ziemke et al. The two approaches are analyzed over a sub-regional area around a district, focusing on a suburban city with an undulating relief made of average-to-steep hills. The focus is on the bicycle transportation model because the catchment area has a particularly challenging altitude profile and a large variety of roads, whether in type—from residential to national highway—or in pavement surface due to the number of green areas, such as parks and forests. This area is defined as a rather large 7 × 12 km, including five suburban cities in the South of Paris, France. A synthetic population of 126,000 agents was generated at a regional scale, with chains of activity made of work, education, shopping, leisure, restaurant and kindergarten, with activity-time choice, location choice and modal choice. We wanted to know how accurately a standard model of bicycle travels can be made with a 2D flat Earth assumption by comparing it to an algorithm extension that explicitly considers road characteristics in cyclists’ route choices. Our finding is that the MATSim bicycle extension model impacts mainly the long trips. Otherwise, the differences are minimal between the two models in terms of travel time and travel distance. Full article

Annotated datasets play a significant role in developing advanced Artificial Intelligence (AI) models that can detect bridge structure defects autonomously. Most defect datasets contain visual images of surface defects; however, subsurface defect data such as delamination which are critical for effective bridge deck evaluations are typically rare or limited to laboratory specimens. Three Non-Destructive Evaluation (NDE) methods (Infrared Thermography (IRT), Impact Echo (IE), and Ground Penetrating Radar (GPR)) were used for concrete delamination detection and reinforcement corrosion detection. The authors have developed a unique NDE dataset, Structural Defect Network 2021 (SDNET2021), which consists of IRT, IE, and GPR data collected from five in-service reinforced concrete bridge decks. A delamination survey map locating the areas, extent and classes of delamination served as the ground truth for annotating IRT, IE and GPR field tests’ data in this study. The IRT were processed to create an ortho-mosaic maps for each deck and were aligned with the ground truth maps using image registration, affine transformation, image binarization, morphological operations, connected components and region props techniques to execute a semi-automatic pixel–wise annotation. Conventional methods such as Fast Fourier transform (FFT)/peak frequency and B-Scan were used for preliminary analysis for the IE and GPR signal data respectively. The quality of NDE data was verified using conventional Image Quality Assessment (IQA) techniques. SDNET2021 dataset consists of 557 delaminated and 1379 sound IE signals, 214,943 delaminated and 448,159 sound GPR signals, and about 1,718,083 delaminated and 2,862,597 sound IRT pixels. SDNET2021 addresses one of the major gaps in benchmarking, developing, training, and testing advanced deep learning models for concrete bridge evaluation by providing a publicly available annotated and validated NDE dataset. Full article