Deck Conditions Research Articles

An Optimization Method for Location-Routing of Cruise Ship Cabin Materials Considering Obstacle Blocking Effects

This study examines the challenges related to the storage and distribution of cabin materials on board during the construction of cruise ships. The construction process of cruise ships involves the assembly of various cabin materials, which are temporarily stored and transported in a complex deck environment with multiple obstacles. These processes can be viewed as location-routing problems (LRPs). However, existing solutions for LRPs do not account for the obstacles commonly encountered in deck environments. Therefore, this paper introduces a new variant of LRPs, termed as the BE-LRP (blocking environment location-routing problem). Initially, by considering the optimization objectives of minimizing the distribution distance and the number of vehicles required, the paper develops an optimization model for the location-routing of on-board materials while taking obstacles into consideration. Subsequently, a method for estimating blocked distances is proposed. This method utilizes Gaussian process regression to predict blocked distances and enables the rapid estimation of distances obstructed by obstacles between different destinations. Lastly, a hybrid obstacle blocking distance processing HO-NSGAII algorithm is formulated to address the BE-LRP. Experimental comparisons demonstrate that the distribution scheme derived from this research method does not necessitate modifications to the delivery distance when compared to the traditional NSGAII algorithm, and better aligns with the actual deck conditions. With an equivalent number of delivery vehicles, the scheme results in shorter delivery distances. The delivery distance can be reduced by 3–17% under varying circumstances. These findings are advantageous for enhancing the efficiency of cruise ship construction and mitigating the impact of warehousing and distribution on construction progress.

Automated data-driven condition assessment method for concrete bridges

This paper addresses the challenge of automating data-driven condition assessments for concrete bridges, hindered by limited data availability. The developed method consists of (1) data integration and standardization, and (2) condition assessment modules. The first module captures, structures, and integrates bridge data from diverse sources, including inspection reports, using web scraping and rule-based data extraction. It standardizes inspection data through text mining and natural language processing. The second module employs Bayesian belief networks to assess bridge deck conditions, leveraging standardized data. The method was validated on a set of bridges in Québec, Canada, resulting in a structured, integrated data repository with standardized data from 2255 inspection reports. This repository supports the development of advanced asset management tools for this class of bridges. The method achieved 94.6% accuracy, 95.0% precision, 94.7% recall, and 94.1% F1 score, demonstrating its potential to help transportation agencies improve bridge data and asset management efficiency.

Machine learning approach for predicting bridge components’ condition ratings

Information on bridge condition rating is critical to make decisions regarding rehabilitation or replacement of bridges. Currently, bridge components’ condition ratings are evaluated manually using inspection reports. Markov chain and Petri net models are most commonly used for predicting future values of bridge parameters, however, applicability of these models for a regional or statewide portfolio of bridges may be limited. The existing data based models have low prediction accuracy. Hence, a data and machine learning based approach is presented herein for predicting the future condition values of major components—deck, superstructure and substructure—in a portfolio of bridges with an objective to develop a more accurate approach. National Bridge Inventory (NBI) was used to get information on current and past bridge components’ condition from year 1992–2019 along with other parameters such as ownership, maintenance responsibility and age. After selecting important parameters, this data was used to train three RUSBoost based random forest models for predicting future values of deck, superstructure, and substructure conditions, respectively. The prediction accuracy of the developed models were found above 93%, thereby addressing the limitation of poor prediction accuracy of the existing studies. Additionally, the uncertainties associated with the random forest based predictions were quantified at the regional level and for individual bridges. On-system concrete pre-cast slab units and steel I-beam bridges in Louisiana were selected to demonstrate the proposed approach and predict bridge components condition ratings for years 2020 and 2021.

Техническое обслуживание и ремонт цапф барабанной мельницы плавучего комплекса горного оборудования

Introduction. In order to develop promising deposits of non-ferrous metals in the Arctic zone, in particular the Pavlovskoye lead-zinc deposit on Novaya Zemlya, it is necessary to develop climate-saving geo-technologies for mining. In this connection, a concept of conservation geo-technology has been developed and proposed through the use of a specially designed floating complex of mining equipment, included together with the quarry equipment in a single technological chain for the extraction of ore raw materials and its processing into concentrate. However, for the full functioning of the concept of this geo-technology, it is necessary to carry out preventive maintenance and repair of mining equipment at the site of its operation on a floating complex. Research methods and materials. The methodological basis of the work is a systematic approach to the studied means of carrying out repair and restoration works of mining machinery and equipment, which includes both theoretical analysis and generalization of the results of fundamental and applied works of domestic and foreign authors, including methods and theories of designing technology for repairing metalworking and mining processing complexes, multicriteria analysis and simulation modeling. Research results. The article proposes a new technology for the implementation of repair work to restore the working surfaces of hollow loading and unloading support trunnions of hydrostatic sliding bearing units of a drum mill in cramped deck conditions of a floating complex of mining equipment for the development of deposits in the Arctic zone. The structure of the built-in mechanized modular machine tool complex is developed and presented, with the help of which the hollow support trunnions of the drum mill are directly machined according to the patented repair technology. Conclusion. It is shown that the use of the described repair technology by means of an embedded mechanized modular complex will reduce the complexity of the overhaul of the trunnions without the need to perform dismantling work to disconnect the mill drum body from the hollow trunnion caps, which has a positive effect on the accuracy of processing the working surfaces of hollow trunnions.

Optimizing Machine Learning Algorithms for Improving Prediction of Bridge Deck Deterioration: A Case Study of Ohio Bridges

The deterioration of a bridge’s deck endangers its safety and serviceability. Ohio has approximately 45,000 bridges that need to be monitored to ensure their structural integrity. Adequate prediction of the deterioration of bridges at an early stage is critical to preventing failures. The objective of this research was to develop an accurate model for predicting bridge deck conditions in Ohio. A comprehensive literature review has revealed that past researchers have utilized different algorithms and features when developing models for predicting bridge deck deterioration. Since, there is no guarantee that the use of features and algorithms utilized by past researchers would lead to accurate results for Ohio’s bridges, this research proposes a framework for optimizing the use of machine learning (ML) algorithms to more accurately predict bridge deck deterioration. The framework aims to first determine “optimal” features that can be related to deck deterioration conditions, specifically in the case of Ohio’s bridges by using various feature-selection methods. Two feature-selection models used were XGboost and random forest, which have been confirmed by the Boruta algorithm, in order to determine the features most relevant to deck conditions. Different ML algorithms were then used, based on the “optimal” features, to select the most accurate algorithm. Seven machine learning algorithms, including single models such as decision tree (DT), artificial neural networks (ANNs), k-nearest neighbors (k-NNs), logistic regression (LR), and support vector machines (SVRs), as well as ensemble models such as Random Forest (RF) and eXtreme gradient boosting (XGboost), have been implemented to classify deck conditions. To validate the framework, results from the ML algorithms that used the “optimal” features as input were compared to results from the same ML algorithms that used the “most common” features that have been used in previous studies. On a dataset obtained from the Ohio Department of Transportation (ODOT), the results indicated that the ensemble ML algorithms were able to predict deck conditions significantly more accurately than single models when the “optimal” features were utilized. Although the framework was implemented using data obtained from ODOT, it can be successfully utilized by other transportation agencies to more accurately predict the deterioration of bridge components.

Effect of the incident wind angle in a frigate with Coanda active flow control

The aerodynamic environment around frigates can be complex. This can cause helicopter pilots operating on frigates to face risky situations during the recovery maneuver. In this paper, the introduction of active flow control using Coanda effect has been tested to improve flow characteristics above a frigate flight deck. Wind tunnel tests with Particle Image Velocimetry (PIV) have been performed in order to analyze the effectiveness of introducing Coanda effect at the hangar at four different wind over deck conditions. Applying Coanda effect on the upper part of the frigate hangar the re-attachment distance, and the recirculation areas are reduced. When the Coanda effect is applied also on the sides, the flow recirculation can be completely eliminated, especially for WOD angles different from 0°. The profiles of velocities at the height where the helicopter rotor operates are also corrected, making them uniform and similar to the free stream velocity. In exchange, there is an increase in turbulence intensity close to the hangar, without affecting the rest of the flight deck. The results have shown that Coanda effect clearly improves the aerodynamics over the flight deck where the helicopter operates, and it could help to increase the safety of helicopter operations around frigates.

Investigating Factors Influencing Deck Conditions of Concrete Bridge and Steel Bridge Using an Interpretable Machine Learning Framework

Good deck condition is critical for a bridge to transport vehicles across physical obstacles on the landscape. This study uses the National Bridge Inventory data provided by LTBP InfoBridge to assess the influence of weather, structure, and traffic factors on deck conditions of concrete and steel bridges in the ten states with the highest percentage of bridges with poor deck conditions. For this analysis, an interpretable machine learning framework takes advantage of the high prediction accuracy while maintaining interpretability. A model is built for each structure type producing results that reveal the impacts of the factors on deck conditions for both types of bridges. The effects of various factors on the deck conditions of steel and concrete bridges are analyzed by comparing the similarities and differences among these factors. The results show that the concrete cast-in-place is the only deck structure associated with good deck conditions for steel bridges, while multiple other structure types associate with good deck conditions for concrete bridges. For the wearing surface, the bituminous wearing surface is highly associated with poor deck conditions of steel bridges. The number of snow days is negatively associated with the deck condition of steel bridges, while the concrete bridge could maintain a good performance with extremely long snow days. The findings also confirmed that increasing the truck percentage could deteriorate the deck conditions faster.

Data Fusion of Non-destructive Testing Methods for Bridge Deck Condition Assessment

Background: Highway agencies explore the use of non-destructive testing (NDT) for assessing the condition of their infrastructure in the most cost-effective manner. NDTs can provide as-built construction quality, as well as in service condition assessment especially for older structures where some critical information and/or maintenance records are often not available. In many cases, the use of NDTs requires “ground true” data for either calibration or validation. Surveys for obtaining such information is often challenging for in service structures, time consuming and costly. Aim: Objective of this paper is to present a data fusion approach for forensic investigation of bridge decks, in which Ground Penetrating Radar (GPR) is used to assess the current condition combined with a Laser Scanner system to verify the bridge design features. Methods: The data fusion approach proposed in this study considers using GPR based condition data combined with geometric measurements of the bridge deck obtained from the Laser Scanner system. Such novel fusion approach permits to more accurately measure the current bridge deck conditions in terms of thickness of the bridge deck and rebar depth to assess deterioration. Furthermore, detection of variations in the rebar depth suggested the implementation of a correction procedure for the reflection amplitude of the GPR signal based on true rebar depths instead of the two-way travel time, which is commonly applied. Results: This innovative analysis approach provided a more refined assessment of delaminated areas in the bridge deck. The data fusion and suggested analysis methodology was tested on a pre-stressed concrete bridge in Italy. Discussion: The approach proposed in this study can be used elsewhere for assessing the condition of bridge decks. Conclusion: The merging of such methods is particularly valuable for bridges were construction drawing and records may not be available, and/or there is potential concern of a deviation between design and as-built characteristics (i.e., where quality assurance and quality control records may be questionable).

Decision-making process for maintenance of concrete bridge deck with asphalt overlays using automated digital road scanner

This study proposes a decision-making process for the maintenance of asphalt overlays on concrete bridge decks based on interface conditions evaluated by a non-destructive method. Field data, including crack ratio, rutting depth, surface roughness (IRI, international roughness index), and relative permittivity, were used as variables to evaluate the asphalt bridge-deck pavement conditions at twelve locations (total length of 5268 m) using a vehicle-type automated digital road scanner. The relative permittivities of the concrete bridge decks were measured using a ground penetrating radar scanner on the surfaces of the asphalt layers, and the deck conditions were verified after the removal of the asphalt layers. Through field investigation, 1312 data sets were collected, and statistical analyses were performed to identify characteristics of the variables for evaluating the bridge-pavement condition. Based on the field data and Seoul pavement index formula, the bridge pavement index, which can reflect the condition of the concrete bridge deck beneath the asphalt layer, was developed. Furthermore, a decision-making tree was developed to select appropriate rehabilitation methods depending on various distress types and seriousness, as determined by field data.

Hybrid feature selection framework for predicting bridge deck conditions

Bridge decks’ maintenance funding requirements are influenced by bridge decks' current and predicted future conditions. Additionally, the serviceability of bridges may be negatively impacted by the degradation of bridge decks. Bridge inspections require considerable effort, time, cost, and resources; besides, such inspections may introduce hazards and safety concerns. This paper introduces a data-driven hybrid feature selection framework for predicting bridge deck deterioration conditions and applying it to a bridge deck in Iowa State, USA. Firstly, the Boruta algorithm, stepwise regression, and multi-layer perceptron are employed to find the best subset of features that contribute to bridge deck deterioration. Then, four classification models were developed using the best feature subset of features, namely k-nearest neighbours, random forest, artificial neural networks, and deep neural networks. The hyperparameters of the models were optimized to get their best performance. The developed models showed comparable performance, and the random forest model outperformed the other models in prediction accuracy with fewer misclassifications. The developed models are thought to reduce field inspections and give insights into the most influential factors in bridge deck deterioration conditions.

Fusion of NDE Scanning and Photogrammetric Data for Concrete Bridge Decks and Girders Condition Assessment

The first case history would present 3-D results of Impact Echo Scanning and Ground Penetrating Radar (GPR) Scanning plus Spectral Analyses of Surface Waves tests of post-tensioned concrete bridge girders. The NDE results were overlaid on photogrammetric images of visible corrosion/cracking damage to the concrete bridge girders of an aging bridge due to de-icing salts and a marine environment. Cart-based scanning with GPR and Impact Echo and Spectral Analyses of Surface Waves NDE methods is commonly being conducted of both bare concrete decks and asphalt overlaid decks to map both top and bottom delaminations and depth of concrete cover. The data is being used in conjunction with chlorides and carbonation testing of concrete cores to recommend repairs and evaluate remaining service lives for bridges. Photogrammetric images of bare concrete in particular are being used to visibly compare areas of spalling, delamination and deck patching for improved interpretation, visualization and mapping of internal deck delaminations and other concrete damage with this data fusion approach to provide more accurate surface and full-depth images of bridge deck conditions.

Bridge Deck Deterioration: Reasons and Patterns

The deck condition of bridges is one of the most important factors impacting the connectivity and efficiency of transportation networks. Bridges with quickly deteriorating deck conditions are a huge financial burden for transportation agencies and can downgrade the efficiency of the whole transportation network. This study utilizes an interpretable machine learning framework, Shapley additive explanation (SHAP), to investigate the associations between various factors, such as wearing surface, deck structure, and so forth, and bridges with quickly deteriorating deck conditions nationwide. An XGBoost model is trained to perform the binary classification task on a heavily imbalanced dataset and classify relatively young bridges (less than 20 years old) with poor/fair deck conditions and relatively old bridges (30–40 years old) with good deck conditions in the National Bridge Inventory (NBI) database. The accuracy of the predictive model is 0.91, and the AUC score is about 0.83. After applying this well-performed predictive model on the interpretable machine learning framework, the results revealed that without wearing surface, corrugated steel deck structure, wide bridge structure, and long span are highly associated with bridges with quickly deteriorating decks. The results also show that bridges with a relatively low average daily traffic (ADT) or truck percentage of ADT are in a dilemma zone, where the overall traffic or truck volume of the bridge is not low enough to prevent fast deterioration, but not high enough for eligibility for the funding required for more durable materials during construction or appropriate maintenance.

Bore Impact on Decks of Coastal Structures

Bore impact on horizontal fixed decks of coastal structures is studied by use of the Level I Green–Naghdi (GN) equations and the Navier–Stokes (NS) equations. The bore is generated by the breaking of a water reservoir, and may represent the propagation of a tsunami on land or broken storm waves. The bore-induced horizontal and vertical forces are determined and their variation with the bore and deck conditions is studied in this work. Various conditions of deck location with respect to the water level are considered, including cases with the deck under or above the still-water level. Two types of bore are considered, namely (i) a bore generated by a dam break, where the reservoir water depth is substantially larger than the downstream depth, and (ii) a bore generated by an initial mound of water, where the reservoir water depth is comparable to the downstream depth. It is shown that these mechanisms result in the formation of significantly different bore shapes. It is also shown that the relative height of the reservoir and the downstream water depth play a significant role in the bore generation and its impact on coastal structures. It is also found that the bore-induced forces vary almost linearly with the change in amplitude of the reservoir, while a change in the length of the reservoir has little effect on the loads. The horizontal force on submerged decks is shown to be independent of the submergence depth of the deck; this is due to the uniform velocity distribution over the water column of the bore. Results of the GN and NS models are compared with each other for submerged cases and the limitations, accuracy, and efficiency of these models in studying this problem are discussed. Results of the GN equations are in close agreement with the NS equations, making them a computationally efficient alternative for the study of this problem.

Condition Assessment Using None Destructive Tests at Montauk Bridge Deck

Abstract: Ground penetrating radar (GPR) and portable seismic property analyzer (PSPA), was used in concrete structures for monitoring, quantifying, and mapping the deterioration of bridge decks. The Montauk Bridge deck was assessed based on PSPA and GPR data. Based on the analysis of the PSPA data, it was determined that over 65% of bridge conditions were rated serious to poor condition with an average compressive strength of less than 2500 psi; less than 35% of bridge deck conditions were rated fair to good with an average compressive strength over 2500 psi. Based on GPR data, it was determined that 72% of the bridge deck was in serious to poor condition, and only 28% of the bridge deck was in fair to good condition. Additionally, the analyses of the ground penetrating radar data indicated possible rebar corrosion in places. For these reasons, it is recommended that the Montauk bridge’s deck be completely replaced. Keywords: Condition assessment, NDT, GPR, PSPA, bridge deck

A comparison of helicopter recovery maneuvers on frigates by means of PIV measurements

Helicopter pilots who have to operate on frigates must deal with turbulent and unsteady flows caused by the incidence of the wind on the non-aerodynamic surfaces of the frigate. In addition, the flight deck is usually placed at the stern, right behind the frigate superstructure where recirculation and low-velocity regions appear. For that reason, studying how the aerodynamic effects influence the helicopter rotor during the approach and landing on the frigate could be essential to guarantee safe operations. The aim of this paper is to present PIV velocity maps with the flow patterns generated by the interaction between the frigate wake and the helicopter rotor working during its landing approach usually named as dynamic-interface. Three different landing maneuvers have been analyzed: rear, diagonal and aft-fore. The jet generated by the rotor will be used to obtain induced velocity profiles under the rotor. With these velocity profiles, rotor asymmetries during its working over the frigate are detected. With all data at each point of the maneuvers, a final comparison will be made to select the best procedure for helicopter recovery on frigates under different wind over deck conditions.

CAA research programme – Helicopter Operations to Moving Offshore Helidecks

ABSTRACTThe oil and gas industry relies heavily on helicopters for transporting personnel and cargo to and from offshore installations and support vessels. A growing number of offshore helicopter operations are to moving helidecks, which include large vessels such as FPSOs, drill ships, and semi-submersibles, as well as smaller service vessels. Landing a helicopter on a moving helideck presents additional challenges to those faced on fixed helidecks, not only at the point of touchdown but also for the entire period the helicopter remains on the helideck.The UK Civil Aviation Authority (CAA), on behalf of the joint CAA/industry Helicopter Safety Research Management Committee has led a comprehensive programme of research over a number of years, aimed at improving the operational safety of helicopters landing on moving helidecks. The work focused on the aspect of the stability of helicopters once landed on a moving helideck, this being the main source of in-service incidents and accidents as evidenced in the Mandatory Occurrence Reports. The project culminated in the development of a new standard for Helideck Monitoring Systems (HMS), which was published by the Helideck Certification Agency in April 2018 with an implementation compliance date of 31 March 2021. Operations to moving helidecks not equipped with HMS meeting the new standard will be restricted to stable deck conditions from this date. The research underpinning the new standard is presented in two papers.This paper (Part A), presents the analytical approach that has been developed to model the Reserve of Stability (ROS) for all modes of failure of a helicopter on a moving offshore helideck.The analytical model covers all modes of on-deck failure (roll-over and sliding), for any nose wheel tricycle undercarriage helicopter. The mathematical expressions that have been derived are remarkably simple, physically intuitive, and make the relative contribution of all the destabilising factors easy to understand and assess. These analytical expressions can be used to calculate the ROS of any helicopter in real time, as well as for calculating an envelope of safe operating limits.This approach has many advantages compared to conventional ‘black box’ modelling methods. The main advantage is that it simplifies the modelling of the destabilising effect of helideck motion and allows the most salient parameters governing on-deck ROS to be defined, namely the Measure of Motion Severity, the instantaneous wind speed as the Measure of Wind Severity, and the wind direction relative to the helicopter (Relative Wind Direction).The main rotor lift generated during the time the helicopter remains on-deck (at Minimum Pitch on Ground, MPOG) has been one of the most important unknowns that this research programme has sought to address. An empirical model for estimating the lift at MPOG has been developed, based on experimental and field data. Practical methods for quantifying fuselage wind drag and the vertical position of the centre of gravity were also developed, which allow different helicopter types to be assessed without recourse to helicopter Original Equipment Manufacturer (OEM) proprietary information or models. Finally, a comparison and evaluation of the model against dedicated field trial measurements is presented, together with a discussion of the modelling strengths and weaknesses, and recommendations for further work.

Evaluating Concrete Damage in Bridge Decks with and without Overlays Using Nondestructive Testing Procedures

Abstract The state of Wyoming alone has 13.1 million square feet of bridge deck, and evaluation of those decks has become an important part of the Wyoming Department of Transportation’s (WYDOT) management of bridge repairs. The authors believe that development and advancement of nondestructive evaluation methods over the past 25 years may provide a more efficient, standardized, and accurate method for evaluating bridge deck conditions compared with current practices. A study was performed on three bridge decks in Wyoming: the First Street Bridge in Casper, the Douglas I-25 Bridge, and the Remount I-80 Bridge. For each bridge, an investigation was done using standard WYDOT practices for chain dragging. In addition, the bridges were evaluated using impact echo, thermal imaging, and ground-penetrating radar (GPR) techniques. All three methods considered were successful, and the damage locations between the impact echo, thermal imaging, and GPR generally correlated well. Based on this study, a complete bridge deck evaluation should combine impact echo with GPR testing to provide the most accurate predictions of delamination and debonding in support of optimal maintenance decisions.

Development of Novel Methodology for Assessing Bridge Deck Conditions Using Step Frequency Antenna Array Ground Penetrating Radar

AbstractGround penetrating radar (GPR) has emerged during the last decade as a nondestructive testing method for assessing the subsurface condition of infrastructure components. However, the use of...

Wind flow investigation over an aircraft carrier deck by PIV

An aircraft carrier is characterized by a superstructure where different systems are installed. This configuration is similar to a set of bluff bodies with sharp edges where massive flow separation appears in the wake. Depending on the wind-over deck (WOD) conditions, the flow field changes dramatically. Hence, aircraft performances are affected due to the adverse aerodynamic effects generated. Flight envelopes are needed to determine aircraft operating range. In situ measurements are costly and time-consuming. Numerical simulations are providing some solutions to this problem. However, they have to be validated by matching simulation results to those from experimental measurements. In the present paper, the study of the flow configuration at different wind angles has been conducted. Particle image velocimetry technique was used to obtain flow information due to the accurate results provided. Non-dimensional velocity and turbulence maps are presented in order to provide flow field characterization.

Numerical simulation of submerged flow bridge scour under dam-break flow using multi-phase SPH method.

This paper presents a coupled two-phase flow model for simulation of submerged flow bridge scour under dam-break flows considering the sediment-fluid interaction. The Smoothed Particle Hydrodynamics (SPH) method is employed to simulate the sediment and fluid movements based on the Newtonian and non-Newtonian fluids, respectively, in the framework of two-phase flow modeling. The SPH simulation based on the treatment of Bingham-type Herschel-Bulkley-Papanastasiou constitutive model and the Drucker-Prager yield criterion is used to predict the sediment transport and the scour depth time histories under a submerged bridge deck. The influence of parameters such as geometry of the bridge deck and flow conditions on the scour depth is also investigated.