Integrating Non-Destructive Surveys into a Preliminary BIM-Oriented Digital Model for Possible Future Application in Road Pavements Management

The implementation of the digitalization of the linear infrastructure is growing rapidly and new methods for developing BIM-oriented digital models are increasing. The integration of the results obtained from non-destructive surveys carried out along a road infrastructure in a pavement digital model can be a useful method for developing an efficient process from a pavement management systems (PMS) point of view. In fact, several applications to optimize PMS have been thoroughly investigated over the years and several researchers and scientists have investigated significant elements for improving the PMS applied to a transport network, including road infrastructures. This study presents a new, tentative process for implementing into a BIM environment the dataset processed from two surveys carried out in a case study. Moreover, the main reason for this investigation is related to the need for an effective system able to evaluate continuously the pavement conditions and programming maintenance interventions. To date, both the instruments and the methods to detect the pavement configuration have evolved, along with the development of non-destructive technology (NDT) tools such as laser-scanners and ground-penetrating radar. Finally, the main results of the research demonstrate the possibility to provide a digital twin model from the synergistic use of geometric and design information with the results from monitoring conducted on a road infrastructure. The model can be potentially used in future BIM-based PMS applications.

Joint Layout Design: Finding the Strongest Connections within Segmental Masonry Arched Forms

Segmental arched forms composed of discrete units are among the most common construction systems, ranging from historic masonry vaults to contemporary precast concrete shells. Simple fabrication, transport, and assembly have particularly made these structural systems convenient choices to construct infrastructures such as bridges in challenging environmental conditions. The most important drawback of segmental vaults is basically the poor mechanical behaviour at the joints connecting their constituent segments. The influence of the joint shape and location on structural performances has been widely explored in the literature, including studies on different stereotomy, bond patterns, and interlocking joint shapes. To date, however, a few methods have been developed to design optimal joint layouts, but they are limited to extremely limited geometric parameters and material properties. To remedy this, this paper presents a novel method to design the strongest joint layout in 2D arched structures while allowing joints to take on a range of diverse shapes. To do so, a masonry arched form is represented as a layout of potential joints, and the optimization problems developed based on the two plastic methods of classic limit analysis and discontinuity layout optimization find the joint layout that corresponds to the maximum load-bearing capacity.

Latest Advances in Finite Element Modelling and Model Updating of Cable-Stayed Bridges

As important links in the transport infrastructure system, cable-stayed bridges are among the most popular candidates for implementing structural health monitoring (SHM) technology. The primary aim of SHM for these bridges is to ensure their structural integrity and satisfactory performance by monitoring their behaviour over time. Finite element (FE) model updating is a well-recognised approach for SHM purposes, as an accurate model serves as a baseline reference for damage detection and long-term monitoring efforts. One of the many challenges is the development of the initial FE model that can accurately reflect the dynamic characteristics and the overall behaviour of a bridge. Given the size, slenderness, use of long cables, and high levels of structural redundancy, precise initial models of long-span cable-stayed bridges are desirable to better facilitate the model updating process and to improve the accuracy of the final updated model. To date, very few studies offer in-depth discussions on the modelling approaches for cable-stayed bridges and the methods used for model updating. As such, this article presents the latest advances in finite element modelling and model updating methods that have been widely adopted for cable-stayed bridges, through a critical literature review of existing research work. An overview of current SHM research is presented first, followed by a comprehensive review of finite element modelling of cable-stayed bridges, including modelling approaches of the deck girder and cables. A general overview of model updating methods is then given before reviewing the model updating applications to cable-stayed bridges. Finally, an evaluation of all available methods and assessment for future research outlook are presented to summarise the research achievements and current limitations in this field.

Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and Analysis

Fatalities and serious injuries still represent a significant portion of run-off-the-road (ROR) collisions on highways in North America. In order to address this issue and design safer and more forgiving roadside areas, more empirical evidence is required to understand the association between roadside elements and safety. The inability to gather that evidence has been attributed in many cases to limitations in data collection and data fusion capabilities. To help overcome such issues, this paper proposes using LiDAR datasets to extract the information required to analyze factors contributing to the severity of ROR collisions on a localized collision level. Specifically, the paper proposes a new method for extracting pole-like objects and tree canopies. Information about other roadside assets, including signposts, alignment attributes, and side slopes is also extracted from the LiDAR scans in a fully automated manner. The extracted information is then attached to individual collisions to perform a localized assessment. Logistic regression is then used to explore links between the extracted features and the severity of fixed-object collisions. The analysis is conducted on 80 km of roads from 10 different highways in Alberta, Canada. The results show that roadside attributes vary significantly for the different collisions along the 80 km analyzed, indicating the importance of utilizing LiDAR to extract such features on a disaggregate collision level. The regression results show that the steepness of side slopes and the offset of roadside objects had the most significant impacts on the severity of fixed-object collisions.

Framework for Design of Sustainable Flexible Pavement

The conventional methodologies for the design of flexible pavements are not adequate in providing solutions that meet the diverse sustainability challenges. Therefore, developing new methodologies and frameworks for the design of flexible pavement has become a priority for most highway agencies. On the other hand, there is no sound sustainable flexible pavement framework at the design phase that considers the key engineering performance, environmental impact, and economic benefits of sustainability metrics. Hence, premature failure of flexible pavements has become a common problem leading to a growing demand for sustainable pavement. Pavement engineers need to have access to tools that permit them to design flexible pavements capable of providing sustainable solutions under various complex scenarios and uncertainties. Hence, the objective of this study was to develop a resilience analysis framework, probabilistic life cycle assessment (PLCA) framework, and probabilistic life cycle cost analysis (LCCA) framework as the pillars of sustainability. These frameworks were used to develop a single sustainable flexible pavement design framework. The developed framework enables highway agencies to effectively quantify the lifetime sustainability performance of flexible pavements during the design phase in terms of resilience, environmental sustainability, and economic sustainability; and it allows to select the optimum design by comparing alternative design options. The framework will enhance the durability of flexible pavement projects by minimizing the cost, operational disturbance, environmental impact, and supporting the design. Many countries, especially those that fully dependent on the road network as the primary transportation route, may benefit from the sustainability-based road network design, which could ensure dependable market accessibility. The resilience of such a road network may reduce the cost of business activities by minimizing the interruption in surface transportation due to the functional and structural failures resulting from extreme events.

Energy Resilience Impact of Supply Chain Network Disruption to Military Microgrids

The ability to provide uninterrupted power to military installations is paramount in executing a country’s national defense strategy. Microgrid architectures increase installation energy resilience through redundant local generation sources and the capability for grid independence. However, deliberate attacks from near-peer competitors can disrupt the associated supply chain network, thereby affecting mission critical loads. Utilizing an integrated discrete-time Markov chain and dynamic Bayesian network approach, we investigate disruption propagation throughout a supply chain network and quantify its mission impact on an islanded microgrid. We propose a novel methodology and an associated metric we term “energy resilience impact” to identify and address supply chain disruption risks to energy security. The proposed methodology addresses a gap in the literature and practice where it is assumed supply chains will not be disrupted during incidents involving microgrids. A case study of a fictional military installation is presented to demonstrate how installation energy managers can adopt this methodology for the design and improvement of military microgrids. The fictional case study shows how supply chain disruptions can impact the ability of a microgrid to successfully supply electricity to critical loads throughout an islanding event.

Designing a Structural Health Monitoring System Accounting for Temperature Compensation

Structural health monitoring is effective if it allows us to identify the condition state of a structure with an appropriate level of confidence. The estimation of the uncertainty of the condition state is relatively straightforward a posteriori, i.e., when monitoring data are available. However, monitoring observations are not available when designing a monitoring system; therefore, the expected uncertainty must be estimated beforehand. This paper proposes a framework to evaluate the effectiveness of a monitoring system accounting for temperature compensation. This method is applied to the design process of a structural health monitoring system for civil infrastructure. In particular, the focus is on the condition-state parameters representing the structural long-term response trend, e.g., due to creep and shrinkage effects, and the tension losses in prestressed concrete bridges. The result is a simple-to-use equation that estimates the expected uncertainty of a long-term response trend of temperature-compensated response measurements in the design phase. The equation shows that the condition-state uncertainty is affected by the measurement and model uncertainties, the start date and duration of the monitoring activity, and the sampling frequency. We validated our approach on a real-life case study: the Colle Isarco viaduct. We verified whether the pre-posterior estimation of expected uncertainty, performed with the experimented approach, is consistent with the real uncertainty estimated a posteriori based on the monitoring data.

Risk-Based Criticality Assessment for Smart Critical Infrastructures

Today, critical infrastructure is more interconnected, which allows more vulnerabilities in the case of disasters. In addition, the effect of one infrastructure can lead to one or more cascading failures in another infrastructure due to the dependency complexity between them. This article introduces a holistic approach using network indicators and machine learning to better understand the geographical representation of critical infrastructure. Previous work on a similar model was based on a single measure; such as in fashion, this paper introduces four measures utilized to identify the most vital geographic zone in the city. The model aims to increase resilience, focusing on the preparedness phase by assessing the essential nodes of infrastructure, which allows more space to adopt risk mitigation strategies before any disturbance event. Holding in-depth knowledge of the vital zones of small scales and accordingly ranking them will positively improve the overall system resilience.

Performance Evaluation of a Hybrid PSO Enhanced ANFIS Model in Prediction of Traffic Flow of Vehicles on Freeways: Traffic Data Evidence from South Africa

In the last few years, there has been a significant rise in the number of private vehicles ownership, migration of people from rural areas to urban cities, and the rise in the number of under-maintained freeways; all these have added to the perennial problem of traffic congestion. Traffic flow prediction has been recognized as the solution in alleviating and reducing the problem of traffic congestion. In this research, we developed an adaptive neuro-fuzzy inference system trained by particle swarm optimization (ANFIS-PSO) by performing an evaluative performance of the model through traffic flow modelling of vehicles on five freeways (N1,N3,N12,N14 and N17) using South Africa Transportation System as a case study. Six hundred and fifty (650) traffic data were collected using inductive loop detectors and video cameras from the five freeways. The traffic data used for developing these models comprises traffic volume, traffic density, speed of vehicles, time, and different types of vehicles. The traffic data were divided into 70% and 30% for the training and validation of the model. The model results show a positively correlated optimal performance between the inputs and the output with a regression value R2 of 0.9978 and 0.9860 for the training and testing. The result of this research shows that the soft computing model ANFIS-PSO used in this research can model vehicular traffic flow on freeways. Furthermore, the evidence from this research suggests that the on-peak and off-peak hours are significant determinants of vehicular traffic flow on freeways. The modelling approach developed in this research will assist urban planners in developing practical ways to tackle traffic congestion and assist motorists and pedestrians in travel behaviour decision-making. Finally, the approach used in this study will assist transportation engineers in making constructive and safety dependent guidelines for drivers and pedestrians on freeways.

Chloride Induced Corrosion and Carbonation in 3D Printed Concrete

The durability of reinforced concrete structures is dependent on the ability of the concrete cover to combat the ingress of chlorides and carbon dioxide in marine and urban environments. In recent years, interest in additive manufacturing), specifically referring to extrusion based three-dimensional concrete printing (3DCP), has been growing in the construction industry. Despite this being a promising technology that can save construction time, costs and resources, certain issues regarding the lack of fusion between subsequent printed layers have been brought to light. Research has shown that the lack of fusion at the interlayer regions can act as ingress pathways for corrosion contaminants, such as carbon dioxide and chloride aqueous solution, that can cause deterioration. This study investigates the interlayer bond strength (flexural strength) and durability performance of 3D printed concrete subjected to pass times between 0 and 30 min and compares the results to reference cast concrete of the same concrete mixture. The durability study includes Durability Index testing (oxygen permeability, water sorptivity and chloride conductivity index), accelerated concrete carbonation and chloride-induced corrosion. The results show that the cast samples outperform printed samples, yielding greater flexural strength and durability properties, and emphasize the importance of improving the 3DCP interfacial bond. Cast samples are shown to have randomly distributed, compact voids compared to the interconnected and elongated pores located at the interlayer regions of printed samples. In addition, printed samples yield lower interlayer bond strength and durability properties with an increase in pass time, which is attributed to surface moisture evaporation as well as the thixotropic behaviour of the concrete mixture. Good relationships between the mechanical strength and durability performance are also presented.