Search Results (9)

Detecting leakage using a point cloud acquired by mobile laser scanning (MLS) presents significant challenges, particularly from within three-dimensional space. These challenges primarily arise from the prevalence of noise in tunnel point clouds and the difficulty in accurately capturing the three-dimensional morphological characteristics of leakage patterns. To address these limitations, this study proposes a classification method based on XGBoost classifier, integrating both intensity and geometric features. The proposed methodology comprises the following steps: First, a RANSAC algorithm is employed to filter out noise from tunnel objects, such as facilities, tracks, and bolt holes, which exhibit intensity values similar to leakage. Next, intensity features are extracted to facilitate the initial separation of leakage regions from the tunnel lining. Subsequently, geometric features derived from the k neighborhood are incorporated to complement the intensity features, enabling more effective segmentation of leakage from the lining structures. The optimal neighborhood scale is determined by selecting the scale that yields the highest F_1-score for leakage across various multiple evaluated scales. Finally, the XGBoost classifier is applied to the binary classification to distinguish leakage from tunnel lining. Experimental results demonstrate that the integration of geometric features significantly enhances leakage detection accuracy, achieving an F_1-score of 91.18% and 97.84% on two evaluated datasets, respectively. The consistent performance across four heterogeneous datasets indicates the robust generalization capability of the proposed methodology. Comparative analysis further shows that XGBoost outperforms other classifiers, such as Random Forest, AdaBoost, LightGBM, and CatBoost, in terms of balance of accuracy and computational efficiency. Moreover, compared to deep learning models, including PointNet, PointNet++, and DGCNN, the proposed method demonstrates superior performance in both detection accuracy and computational efficiency. Full article

Strategically important objects, such as dams, tunnels, bridges, and others, require long-term structural health monitoring programs in order to preserve their structural integrity with minimal downtime, financial expenses, and increased safety for civilians. The current study focuses on developing a damage detection methodology that is applicable to the long-term monitoring of such structures. It is based on the identification of resonant frequencies from operational modal analysis, removing the effect of environmental factors on the resonant frequencies through support vector regression with optimized hyperparameters and, finally, classifying the global structural state as either healthy or damaged, utilizing the Mahalanobis distance. The novelty lies in two additional steps that supplement this procedure, namely, the nonlinear estimation of the relative effects of various environmental factors, such as temperature, humidity, and ambient loads on the resonant frequencies, and the selection of the most informative resonant frequency features using a non-parametric neighborhood component analysis algorithm. This methodology is validated on a wooden two-story truss structure with different artificial structural modifications that simulate damage in a non-destructive manner. It is found that, firstly, out of all environmental factors, temperature has a dominating decreasing effect on resonance frequencies, followed by humidity, wind speed, and precipitation. Secondly, the selection of only a handful of the most informative resonance frequency features not only reduces the feature space, but also increases the classification performance, albeit with a trade-off between false alarms and missed damage detection. The proposed approach effectively minimizes false alarms and ensures consistent damage detection under varying environmental conditions, offering tangible benefits for long-term SHM applications. Full article

This study examines the potential of urban landscape transformation to generate and develop new heritage and the role of heritage urbanism in an industrial city. It explores whether the changeover of urban heritage districts in Pittsburgh (PA, USA) can give rise to a novel type of urban heritage. Pittsburgh experienced urban development primarily driven by the presence and accessibility of natural resources, rather than favorable geographical conditions: topography characterized by rugged hills, rock formations, rivers, and stream valleys. The integration of the American-style grid within this unique natural environment resulted in intriguing juxtapositions. Consequently, elements such as bridges, viaducts, stairs, tunnels, and historical inclines gained paramount importance in shaping the urban fabric. The city’s remaining preserved or transformed urban heritage is protected through historic districts designated by the Department of City Planning, which enforces specific planning and design guidelines. The study employs a multi-faceted approach combining the concepts of historic stratification (urban palimpsest), integrated urban morphology, space syntax (integration analysis), and heritage urbanism. During the personally conducted long-term fieldwork, the selected case studies described herein (historic districts, university campus, and traditional neighborhood) proved to be the most suitable for demonstrating urban heritage formation through urban landscape transformation. Full article

During the drilling and blasting excavation of neighborhood tunnels, blast-induced vibrations negatively affect the stability of the interlaid rock, particularly for the following tunnel. This paper presents a case study of neighborhood tunnels with small clearance in Shenzhen, China, where the minimum thickness of interlaid rock is only 0.5 m. Therefore, the tunnelling method of the following tunnel should be precisely designed to ensure the safety of surrounding rock. Initially, we investigated the damage mechanism of the interlaid rock under the blasting load from the following tunnel using LS-DYNA R11.1 software. To control the damage of the interlaid rock caused by the following tunnel blasting, the four-part excavation method with a reserved vibration-cushioning layer for the following tunnel is proposed. Subsequently, the analytical stress of the surrounding rock for neighborhood tunnels was obtained by the Schwarz alternating method (SAM). By analyzing the variation patterns with different thicknesses of the cushioning layer, the optimal thickness of the cushioning layer was determined to be 3.0 m. Consequently, a safety excavation partition scheme was implemented for the following tunnel. As a result of this case study, suggestions were identified for the safe excavation of neighborhood tunnels with small clearance. Full article

Surface processes such as coatings, corrosion, photocatalysis, and tribology are greatly diversified by acid–base interactions at the surface overlayer. This study focuses on the action of a metallic copper surface as an electron donor/acceptor related to the inactivation of viruses. It was found that regarding Cu₂O or Cu materials, electrostatic interaction plays a major role in virus inactivation. We applied the TPPE method to clarify the mechanism of electron transfer (ET) occurring at light-irradiated copper surfaces. The TPPE characteristics were strongly influenced by the environments, which correspond to the temperature and environment dependence of the total count of emitted electrons in the incident light wavelength scan (PE total count, N_T), the photothreshold, and further the activation energy (ΔE) analyzed from the Arrhenius plot of N_T values obtained in the temperature increase and subsequent temperature decrease processes. In this study, we re-examined the dependence of the TPPE data from two types of Cu metal surfaces: sample A, which was mechanically abraded in alcohols, water, and air, and sample C, which was only ultrasonically cleaned in these liquids. The N_T for both samples slowly increased with increasing temperature, reached a maximum (N_Tmax) at 250 °C (maximum temperature, T_max), and after that, decreased. For sample A, the N_Tmax value decreased in the order H₂O > CH₃OH > C₂H₅OH > (CH₃)₂CHOH > C₃H₇OH, although the last alcohol gave T_max = 100 °C, while with sample C, the N_Tmax value decreased in the order C₃H₇OH > (CH₃)₂CHOH > C₂H₅OH > CH₃OH > H₂O. Interestingly, both orders of the liquids were completely opposite; this means that a Cu surface can possess a two-way character. The N_T intensity was found to be strongly associated with the change from the hydroxyl group (–Cu–OH) to the oxide oxygen (O²⁻) in the O1s spectra in the XPS measurement. The difference between the above orders was explained by the acid–base interaction mode of the –Cu–OH group with the adsorbed molecule on the surfaces. The H₂O adsorbed on sample A produces the electric dipole –CuO^δ−H^δ+ ⋅⋅⋅ :OH₂ (⋅⋅⋅ hydrogen bond), while the C₃H₇OH and (CH₃)₂CHOH adsorbed on sample C produce RO^−δH^δ+ ⋅⋅⋅ :O(H)–Cu− (R = alkyl groups). Gutmann’s acceptor number (AN) representing the basicity of the liquid molecules was found to be related to the TPPE characteristics: (CH₃)₂CHOH (33.5), C₂H₅OH (37.1), CH₃OH (41.3), and H₂O (54.8) (the AN of C₃H₇OH could not be confirmed). With sample A, the values of N_Tmaxa and ΔEa_Up1 both increased with increasing AN (Up1 means the first temperature increase process). On the other hand, with sample C, the values of N_Tmaxc and ΔEc_Up1 both decreased with increasing AN. These findings suggest that sample A acts as an acid, while sample C functions as a base. However, in the case of both types of samples, A and C, the N_Tmax values were found to increase with increasing ΔE_Up1. It was explained that the ΔE_Up1 values, depending on the liquids, originate from the difference in the energy level of the hydroxyl group radical at the surface denoted. This is able to attract electrons in the neighborhood of the Fermi level of the base metal through tunnelling. After that, Auger emission electrons are released, contributing to the ET in the overlayer. These electrons are considered to have a strong ability of reducibility. Full article

In this paper, we study quantum relativistic features of a scalar field around the Rindler–Schwarzschild wormhole. First, we introduce this new class of spacetime, investigating some energy conditions and verifying their violation in a region nearby the wormhole throat, which means that the object must have an exotic energy in order to prevent its collapse. Then, we study the behavior of the massless scalar field in this spacetime and compute the effective potential by means of tortoise coordinates. We show that such a potential is attractive close to the throat and that it is traversable via quantum tunneling by massive particles with sufficiently low energies. The solution of the Klein–Gordon equation is obtained subsequently, showing that the energy spectrum of the field is subject to a constraint, which induces a decreasing oscillatory behavior. By imposing Dirichlet boundary conditions on a spherical shell in the neighborhood of the throat we can determine the particle energy levels, and we use this spectrum to calculate the quantum revival of the eigenstates. Finally, we compute the Casimir energy associated with the massless scalar field at zero temperature. We perform this calculation by means of the sum of the modes method. The zero-point energy is regularized using the Epstein–Hurwitz zeta-function. We also obtain an analytical expression for the Casimir force acting on the shell. Full article

In this paper, the mechanical response mechanism and damage behavior of a railway tunnel lining structure under reverse fault dislocation were studied. The damage behavior of railway tunnel linings under reverse fault dislocation was validated by undertaking laboratory tests and three-dimensional numerical simulations, where Coulomb’s friction was used in the tangential direction of the interface. The failure damage, which increasingly accumulates with displacements, mainly concentrates in fault fracture neighborhoods 0.5 D to 1.5 D (D is the tunnel diameter) within the footwall. The maximum surrounding rock pressure and the maximum longitudinal strain develop in the tunnel near the hanging wall area. The damage begins as longitudinal cracking of the inverted arch. With the increase in dislocations, those cracks develop upward to the arch foot and the waist. Consequently, those oblique cracks separate lining segments, leading to abutment dislocation. The research results provide technical guidance and theoretical support for on-site construction and follow-up research, and they have important application value. Full article

Different concrete structures (viaducts, bridges, or tunnels) in the neighborhoods of railways may be subject to the stray current leaking from the rails. In these cases, the reinforcing rebars embedded in concrete act as conductors, “pick up” the stray current, and can corrode. For simulating the stray current-induced corrosion of metals, most researchers just supplied anodic polarization on samples. However, stray current induces both cathodic polarization and anodic polarization. This work experimentally justifies the different effects of stray current and anodic polarization on reinforcing steel embedded in mortar. A comparison between stray current and anodic polarization effects on the corrosion behavior of embedded steel is performed for both fresh (24 hour-cured) and hardened matrix (28 day-cured) in chloride-free (Cl-free) and chloride-containing (Cl-containing) environments. It is found that in all studied conditions, anodic polarization leads to a significantly different electrochemical performance of the steel rebar compared to the stray current. Hence, anodic polarization cannot reflect all the effects of stray current, and therefore, it has limited significance for simulating stray current. It is also clarified that the curing regimes and starting time of the stray current play significant roles in the formation of a corrosion product layer on the steel surface. Full article

Monitoring the distribution of vehicles across the city is of great importance for urban traffic control. In particular, information on the number of vehicles entering and leaving a city, or moving between urban areas, gives a valuable estimate on potential bottlenecks and congestions. The possibility of predicting such flows in advance is even more beneficial, allowing for timely traffic management strategies and targeted congestion warnings. Our work is inserted in the context of short-term forecasting, aiming to predict rapid changes and sudden variations in the traffic volume, beyond the general trend. Moreover, it concurrently targets multiple locations in the city, providing an instant prediction outcome comprising the future distribution of vehicles across several urban locations. Specifically, we propose a multi-target deep learning regressor for simultaneous predictions of traffic volumes, in multiple entry and exit points among city neighborhoods. The experiment focuses on an hourly forecasting of the amount of vehicles accessing and moving between New York City neighborhoods through the Metropolitan Transportation Authority (MTA) bridges and tunnels. By leveraging a single training process for all location points, and an instant one-step volume inference for every location at each time update, our sequential modeling approach is able to grasp rapid variations in the time series and process the collective information of all entry and exit points, whose distinct predicted values are outputted at once. The multi-target model, based on long short-term memory (LSTM) recurrent neural network layers, was tested on a real-world dataset, achieving an average prediction error of 7% and demonstrating its feasibility for short-term spatially-distributed urban traffic forecasting. Full article