Automated multi-category tunnel damage detection and report generation from ultra-high-resolution panoramic laser images

In the realm of ageing tunnel infrastructure, accurately assessing structural damage remains a pressing challenge due to the inherent subjectivity and time demands of manual inspections. Although reality capture technology allows for digital representation of as-is condition of assets, converting these rich data sources into actionable risk assessments demands still requires innovative solutions. In this paper, we introduce a comprehensive, web-based automated framework that uses ultra-high-resolution (UHR) panoramic tunnel images to automatically generate detailed damage records and risk assessment reports. A significant challenge in this domain is the observation that damage regions often lack sharply defined boundaries; instead, they exhibit gradual, blurred transitions, which is not well-suited to conventional segmentation evaluation. To address this, we formally define the challenge of inconsistency of damage annotation in complex real-world scenarios and propose a novel evaluation metric: Intersection over Union with buffer zone (IoUb). This metric relaxes the rigid boundary precision requirements of traditional evaluation methods, focusing more on capturing the overall damage. We evaluated several instance segmentation algorithms and recommend adopting a lower confidence threshold, as it reduces missed detections without significantly increasing false positives. We introduce post-processing methods that aggregate the predictions from multiple inferences to meet the demands of processing UHR panoramic images, resulting in a 3% improvement in Macro IoU and IoUb, along with a 90% damage recall. Experimental results on Italian road tunnels demonstrate that our framework enhances automated damage detection. We then categorize damage severity using a statistically grounded methodology, enable natural language queries of statistical damage results, and handle visualization and report export, all within a single end-to-end web-based platform. The proposed framework significantly enhances the efficiency of professionals in planning and monitoring ageing tunnel assets. Our code is available at https://github.com/zxy239/Auto-damage-report-generatio

Bethe ridge electron Compton spectroscopy

Compton spectroscopy measures J(pz), the number density of occupied electronic states with momentum component pz. In a transmission electron microscope (TEM) Compton spectroscopy is performed by acquiring a momentum resolved, dark-field electron energy loss spectrum (EELS). Here it is shown that the Bethe ridge in a single energy filtered diffraction pattern can provide identical J(pz) information. The energy filtered TEM (EFTEM) approach is more dose efficient, since all (projected) momenta pz are recorded in parallel. For weakly diffracting specimens, the J(pz) profiles extracted using EFTEM are in reasonable agreement with dark-field EELS. Bragg diffraction and thermal diffuse scattering are known to introduce artefacts in Compton spectroscopy, and this is true for the EFTEM method as well. The artefacts can however be mitigated by analysing suitably thin specimens

Predicting and planning: An intelligent system for demand-based hotel staff scheduling

This study develops an intelligent staff scheduling system for hotels that integrates customer demand prediction with workforce planning to address contemporary challenges in hospitality management. Drawing on operational efficiency principles, the system anticipates guest demand using a long short-term memory (LSTM) network and generates optimal staff schedules by combining ant colony optimization (ACO) algorithm with variable neighborhood search (VNS) algorithm. The system is designed to balance operational efficiency with employee well-being while minimizing labor costs. Real-world testing demonstrates that the system produces schedules that outperform traditional manual methods. By automating the scheduling process, the approach aligns business objectives with employee satisfaction, resulting in more efficient operations and improved working conditions. This research contributes to the theoretical understanding of operational efficiency and its practical application in hotel staff scheduling by integrating demand forecasting with schedule planning, thereby meeting the needs of customers, managers, and employees

A theoretical appraisal of the GR4J rainfall-runoff modelling framework

The GR4J (Génie Rural à 4 paramètres Journalier) model is a widely used conceptual rainfall-runoff model that simulates river flow from daily precipitation and potential evapotranspiration data. This article presents a theoretical appraisal of GR4J with two primary objectives: (1) to establish clearer links between its heuristic components and physically-based hydrological processes, and (2) to identify practical modifications to minimise the need for operator splitting within its analytical solution framework. We show that GR4J’s soil water model is mathematically identical to the Probability Distributed Model (PDM), although it employs a fixed probability density function that resembles a log-normal distribution, while its empirical unit hydrograph can be moment-matched with a diffusion wave model derived from the Saint-Venant equations. Furthermore, while percolation can be conceptually appropriate for some catchments, we demonstrate that operator splitting can be entirely avoided by omitting percolation, assuming inter-catchment groundwater exchange is a linear function of discharge rate and adopting an alternative exponential storage function analytical solution for the non-linear flow routing store. We propose a revised model structure that is more physically interpretable and computationally efficient than the original GR4J formulation. Testing our modifications across 671 catchments in the UK revealed minimal impacts on model calibration and validation performance. This work provides a deeper theoretical basis for key empirical aspects of GR4J and introduces an operator-splitting-free formulation expected to yield more numerically reliable results

The role of agency theory in stock price crashes during the COVID-19 crisis

This study tests the empirical relevance of agency theory in explaining stock price crashes in U.S. firms. We construct two novel multidimensional indices of managerial opportunism using a broad set of agency-related variables linked to bad news hoarding. Using the COVID-19 pandemic as a natural experiment, we examine whether crisis-induced survival pressures intensify the relation between agency problems and the release of previously withheld bad news. Contrary to expectations, we find no significant association between the agency-based indices and future crashes. These findings challenge the traditional agency-based crash risk explanations and underscore the need to explore alternative mechanisms

A coupled FEM–SBM methodology for dynamic interaction of multiple structures and soil

In densely populated urban areas, there is a growing trend in constructing complex structural systems that include underground structures located beneath clusters of aboveground buildings. The dynamic interaction between these underground and aboveground structures, mediated by the surrounding soil, is known as structure-soil–structure interaction (SSSI). SSSI is a topic whose effects pose significant challenges to the design and analysis of such complex systems. In this paper, we propose a novel numerical methodology for addressing longitudinally invariant multi-structure-soil interaction problems in elastodynamics. The proposed approach combines the Finite Element Method (FEM) for modelling structural components with the Singular Boundary Method (SBM) for simulating wave propagation in the soil and capturing inter-structural coupling effects, all formulated in the wavenumber-frequency domain. The synergy of FEM, well-suited to complex geometries, and the computational simplicity and efficiency of SBM yields a robust and accurate framework for solving SSSI problems. The framework features a strongly coupled formulation between structures and soil, enhancing both accuracy and ease of implementation. The accuracy of the method is assessed through several benchmark studies involving cylindrical shells and cylindrical solids, while its practical applicability is demonstrated via real-scale numerical examples, with relative errors typically below 2%. Furthermore, the computational efficiency of the proposed methodology is compared with traditional hybrid approaches, in which both the structures and the surrounding soil are modelled using FEM, with the remaining soil represented via the Method of Fundamental Solutions (MFS) or Boundary Element Method (BEM). On average, the proposed approach achieves a computational performance nearly twenty times faster than that of the reference solutions. The results underscore the advantages of the proposed framework in terms of modelling simplicity, numerical efficiency, accuracy and robustness, and show that the method is scalable and capable of evaluating interactions among an arbitrary number of structures

Artificial intelligence transformations in geotechnics: progress, challenges and future enablers

Our reliance on the underground space to deliver critical civil engineering infrastructure is growing: to accommodate utility and transport infrastructure in urban environments, to provide innovative housing and commercial solutions, and to support proliferating renewable energy infrastructure, particularly offshore. Artificial intelligence (AI) is arguably the most promising enabler to transform geotechnical engineering by extracting knowledge from data to achieve step-change increases in efficiency, sustainability, reliability and safety. This paper seeks to develop a shared understanding of the state of the art of AI in geotechnics and to explore future developments. By way of example, specific popular use cases in geotechnics are considered to highlight current progress in AI applications including intelligent site investigation, predictive modelling for soil behaviour, and optimisation of design and construction processes. The paper then addresses key research challenges, such as data scarcity and interpretability, and discusses the opportunities that lie ahead in the integration of AI with geotechnical engineering. Finally, priority technological enablers are identified for future transformations