A safety assessment planning strategy proposal within the context of sustainable Urban mobility Plans: How to account for Connected and Autonomous vehicles in safety analysis in the SUMP?

In the context of Sustainable Urban Mobility Plans (SUMP), when road safety assessments are dealt with, different future scenarios are considered weighing the positive impacts of the proposed strategies for improving the transport system and road safety, globally. However, while considering those future scenarios, until now, the chance that Connected and Autonomous Vehicles (CAVs) will be introduced in the market has never been accounted. Neglecting CAVs can provide misleading results in terms of safety assessment. In this study, a general framework about how to include CAVs in SUMP safety assessments is provided. The general framework, which relies on traffic simulations and algorithms to count conflicts, was tested on two-way two-lane rural roads within the Province of Bari (Italy), where a SUMP was recently developed but the possible introduction of AVs has not been accounted, as it is a common practice in SUMP drafting. Results provided by simulations show a dramatic crash reduction when the traffic is made only of CAVs, while more dangerous situations are highlighted in the case of mixed traffic. Therefore, some countermeasures to handle mixed traffic, such as e.g., reserved lanes for CAVs in case of new roads, must be found and provided for stakeholders and practitioners while dealing with planning strategie

Cost-effective monitoring of structural risk of bridges supported by cutting-edge technologies

Bridges and viaducts are crucial components of transportation network, and activities as risk assessment and structural maintenance are crucial to avoid economic losses and casualties due to their collapse. External actions, natural hazards, material degradation and aging affect safety and serviceability of bridges. The effort required by national guidelines, as the one issued in 2020 in Italy, pose questions about the resources required for large-scale screening and risk assessment of large bridge portfolios, mostly populated by simply supported concrete girder bridges, both reinforced and prestressed (PSC). The main challenge is to leverage cutting-edge technologies for a cost-effective near-continuous monitoring of structural risk of bridges. In the field of remote-sensing technologies, Interferometry via Synthetic Aperture Radar and Unmanned Aerial Vehicle photogrammetry are two emerging technologies that meet requirements to achieve the goal. Large coverage, adequate resolution and accuracy, and acquisition frequency are them strengthen points at the base of two proposed frameworks. The combination with basic structural knowledge of bridges and environmental data represents a promising mix for portfolio structural risk assessment of existing bridges and structures as demonstrated in the explored case studies. Although these technologies are affected by limitations, their constant use can help to identify bridges requiring a more refined structural risk assessment based on traditional structural health monitoring systems (i.e., sensor-based). Some bridge typologies as PSC box-girder bridges pose additional challenges requiring more effective strategies to monitor prestressing force reduction. Machine learning surrogate models as Artificial Neural Networks coupled with eXplainability approaches are demonstrated to be effective when integrated with traditional sensor-based systems. The experimental campaign on a scaled PSC box bridge is a great example of this combination. The cost-effective monitoring of structural risk of bridges supported by cutting-edge technologies is a true example of the ongoing change in civil engineering that will flourish in the years to come thanks to the technical and scientific progress

A Matheuristics for the Configuration of Automated Vertical Lift Modules Warehouses

The design of the layout of Vertical Lift Module (VLM) warehouses is a non-trivial process that involves selecting dimensions, internal configuration, and allocation of each tray to avoid space loss while satisfying logistic constraints. Our contribution in this context is a two-phase matheuristics --an algorithm that combines exact mathematical methods and heuristics-- to simplify the design of VLMs layout. The proposed matheuristics relies on three Mixed-Integer Linear Programming models, addressing the internal configuration of trays and the allocation of trays into columns based on industrial logistic constraints. This approach requires as input parameters the items features, predetermined tray types with different dimensions, matheuristic settings, and a priority rule for tray allocation. The algorithm outputs to the logistics operator types and quantities of trays needed, internal partitioning, item positions in each tray, and tray positions in each column. Extensive testing demonstrates the effectiveness of our approach under realistic scenarios. Additionally, we introduce a comprehensive set of priority rules for allocating trays into columns, providing a comparison to assist logistics operators in selecting the most suitable for specific scenarios

Enhancing Maintenance Operations in Industry 5.0: A Conceptual User Interface Design for Task Assignment

The Fifth Industrial Revolution, or Industry 5.0, fosters an innovative, resilient, competitive, and society-centered industry. This era emphasizes enhanced human-machine interactions, enabling individuals to manifest their creativity through personalized products and services. As smart factories evolve, the demand for flexibility and adaptability necessitates increased cognitive efforts, particularly in maintenance tasks critical to the flexibility of production systems. Despite the potential of emerging technologies like Augmented Reality and Artificial Intelligence to aid operators, the complexity of tasks combined with the novelty of such technologies can overwhelm workers, thereby impacting workplace well-being. To tackle these challenges, the DESDEMONA project, funded by the European Union through PRIN as part of NextGenerationEU, is developing a Decision Support System (DSS). This system aims to provide real-time suggestions for assigning the most suitable operators for maintenance tasks characterized by high cognitive demands. The DSS considers three primary factors: the operator’s profile (including skills and age), their emotional state, and the availability of smart devices. This manuscript details the project’s initial results, presenting a simplified mathematical model capable of ranking the optimal list of operators. To demonstrate the effectiveness of the DSS, it is compared, through a simulation approach, with a simulated maintenance supervisor. This comparison highlights the system’s ability to identify, from the k-permutations of N operators, the number of optimal tuples that best fit the operational needs

Optical synchronous signal demodulation-based quartz-enhanced photoacoustic spectroscopy for remote, multi-point methane detection in complex environments

We present a novel optical synchronized signal demodulation (OSSD) method applied in quartz-enhanced photoacoustic spectroscopy (QEPAS) for remote gas sensing. Using 1 % of the laser source as an optical synchronization signal, kilometer-scale remote gas detection was achieved, overcoming the challenges of long-distance real-time detection in complex environments with conventional QEPAS. A time-sharing OSSD-QEPAS system for sewer methane detection was subsequently developed. The system’s modulation depth was optimized, and the catalytic effect of water vapor on photoacoustic signals was validated, resulting in a CH4 sensor achieving a detection limit of 445 ppb with a 300-ms averaging time, and an excellent linear dynamic range with a R2 = 0.999. To demonstrate the stability, robustness, and accuracy of the OSSD-QEPAS system, continuous methane measurements covering a 14-hour period at two different sewer locations on campus were performed