Search results

The purpose of this paper was exploring and comparing different deterioration models based on statistical and machine learning approaches. These models were chosen from their successful results in other case studies. The deterioration models were developing considering two scenarios: (i) only the age as covariate (Scenario 1); and (ii) the age together with other available sewer characteristics as covariates (Scenario 2). Both were evaluated to achieve two different management objectives related to the prediction of the critical condition of sewers: at the network and the sewer levels.

Six statistical and machine learning methods [logistic regression (LR), random forest (RF), multinomial logistic regression, ordinal logistic regression, linear discriminant analysis and support vector machine] were explored considering two kinds of predictor variables (independent variables in the model). The main propose of these models was predicting the structural condition at network and pipe level evaluated from deviation analysis and performance curve techniques. Further, the deterioration models were exploring for two case studies: the sewer systems of Bogota and Medellin. These case studies were considered because of both counts with their own assessment standards and low inspection rate.

The results indicate that LR models for both case studies show higher prediction capacity under Scenario 1 (considering only the age) for the management objective related to the network, such as annual budget plans; and RF shows the highest success percentage of sewers in critical condition (sewer level) considering Scenario 2 for both case studies.

There is not a deterioration method whose predictions are adaptable for achieving different management objectives; it is important to explore different approaches to find which one could support a sewer asset management objective for a specific case study.

The originality of this paper consists of there is not a paper in which the prediction of several statistical and machine learning-based deterioration models has been compared for case studies with different local assessment standard. The above to find which is adaptable for each one and which model is adaptable for each management objective.

This paper aims to introduce a multiscale computational method for structural failure analysis with inheriting simulation of moving trans-scale boundary (MTB). This method is motivated from the error in domain bridging caused by cross-scale damage evolution, which is common in structural failure induced by damage accumulation.

Within the method, vulnerable regions with high stress level are described by continuum damage mechanics, while elastic structural theory is sufficient for the rest, dividing the structural model into two scale domains. The two domains are bridged to generate mixed dimensional finite element equation of the whole system. Inheriting simulation is developed to make the computation of MTB sustainable.

Numerical tests of a notched three-point bending beam and a steel frame show that this MTB method can improve efficiency and ensure accuracy while capturing the effect of material damage on deterioration of components and structure.

The proposed MTB method with inheriting simulation is an extension of multiscale simulation to structural failure analysis. Most importantly, it can deal with cross-scale damage evolution and improve computation efficiency significantly.

Purpose – The purpose of this study is to develop a numerical framework to predict the time-dependent probability of failure of a bridge subjected to multiple vehicle impacts. Specially, this study focuses on investigating the inter-relationship between changes in life-cycle parameters (e.g., damage size caused by vehicle impact, loss of initial structural capacity, and threshold intervention) and bridges probability of failure.

Design/Methodology/Approach – The numerical procedure using MATLAB program is developed to compute the probability failure of a bridge. First, the importance and characteristics of life-cycle analysis is described. Then, model for damage accumulation and life cycle as a result of heavy vehicle impacts is discussed. Finally, the probability of failure of a bridge subjected to vehicle impacts as a result of change in life-cycle parameters is presented.

Findings – The results of study show that damage size caused by both vehicle impacts and loss of initial structural capacity have a great impact on the long-term safety of bridges. In addition, the probability of failure of a bridge under different threshold limits indicates that the structural intervention (e.g., repair or maintenance) should be undertaken to extend the service life of a bridge.

Research Limitations/Implications – The damage sizes caused by heavy vehicle impacts are based on simple assumptions. It is suggested that there would be a further study to estimate the magnitude of bridge damage as a result of vehicle impact using the full-scale impact test or computational simulation.

Practical Implications – This will allow much better predictions for residual life of bridges which could potentially be used to support decisions on health and maintenance of bridges.

Originality/Value – The life-cycle performance for assessing the time-dependent probability of failure of bridges subjected to multiple vehicle impact has not been fully discussed so far.

Synopsis The use of exposed concrete in car‐parking structures is universal. Even in steel‐framed structures, over 90 per cent of the construction material is concrete and the only variation is whether it is precast, prestressed, or ordinary reinforced concrete. Having inspected more than 50 parking structures over the past 17 years, the author has seen many buildings where defects have repeated themselves time and again and where problems which appeared to be of minor import to the original designers and builders eventually developed into major defects.

The purpose of this study is to minimize cost and minimize building condition. Weibull distribution approach was employed to generate deterioration curves over time. The third floor of Concordia University’s Engineering And Visual Arts (EV) Complex in Montreal, Canada, served as a case study to test the maintenance model and determine the optimal maintenance activities to be performed.

This research has demonstrated that there is insufficient fund allocation for the maintenance of non-residential buildings. Therefore, this research focused on designing and developing a maintenance optimization model that provides the type of spaces (architectural system) in a building. Sensitivity analysis was used to calculate weights to validate the model. Particle swarm optimization, based explicitly on multiple objectives, was applied for the optimization problem using MATLAB.

Following 100 iterations, 13 non-dominant solutions were generated. Not only was the overall maintenance cost minimized, but the condition of the building was also maximized. Moreover, the condition prediction model demonstrated that the window system type has the most rapid deterioration in educational buildings.

The model is flexible and can be modified by facility managers to align with the required codes or standards.

The purpose of this paper is to communicate and share experiences with other dam designers, operators and maintenance officers arising from the rehabilitation of the Osborne Dam outlets, pursuant to informing future dam outlet designs and rehabilitation of similar dam outlets.

This paper documents and utilizes actual events that occurred before and during the rehabilitation of the outlets. Some solutions applied were unique, with resultant innovative engineering designs being decided on site. Realizing that a descriptive research format would best serve the sharing of experiences of the outlets’ rehabilitation, the case study approach was selected. Before commencement of the rehabilitation works, the different operational problems that had been identified, photographed and documented by the dam’s operating staff were verified by the dam owner’s engineers, technicians and consultants. Visual and photographic verification was done using divers for areas under water. Literature review was conducted so as to learn from solutions employed elsewhere. A physical model of the solution considered the best way to keep the intake tower dry was made and tested.

The outlets’ rehabilitation works, taking 12 months to complete at a total cost of US$5 million, involved installation of new service gates, replacement of the sleeve valves, refurbishment of the butterfly valves and provision of dewatering pumps.

This paper offers unique experiences and lessons for dam designers and other dam operation and maintenance officers while contributing to the body of knowledge of outlet works rehabilitation.

The purpose of this paper is to review the Fuzzy Markov development for assessing the structural integrity of buried transportation bridges. In doing so, the appropriateness of Fuzzy Markov will be assessed, leading to the subsequent model.

This research will utilize the Fuzzy Markov techniques as the conceptual framework. Such methodology is further supported via the utilization and evaluation of 30 buried transportation bridges using the developed Fuzzy Markov model.

Subsequently, through a developed Fuzzy Markov model, this research found that as the basis of structural resilience, specific matrices for age-dependent transition probability can be compiled using conditional survival probabilities in the various structural states; as the basis of structural integrity, specific environmental and economic schemes can also be established based on inspection intervals, intervention systems and failure phases; exact inspection and maintenance intervals can be scheduled to further prolong an asset’s life; and clear and early warning signs can also be formulated for immediate intervention when the structural integrity of the asset are indeed compromised.

The gap within the literature currently surrounds the limitation of computational analysis for some buried structures such as bridges. Specifically, to streamline such evaluation and regimes, a Fuzzy Markov is developed and reviewed.

In this study we examine the spectral properties of stiffness degradation at the constitutive level and at the levels of finite elements and their assemblies. The principal objective is to assess the effects of defects on the elastic stiffness properties at different levels of observation. In particular, we are interested in quantitative damage measures, which characterize the fundamental mode of degradation in the form of elastic damage at the level of constitutive relations and at the level of finite elements and structures.

The purpose of this paper is to further validate a wireless sensor system developed at Clarkson University for structural monitoring of highway bridges. The particular bridge monitored employs a fiber reinforced polymer (FRP) panel system which is fairly innovative in the field of civil engineering design. The superstructure was monitored on two separate occasions to determine a change in structural response and see how the structural system performs over time.

A series of wireless sensor units was deployed at various locations of the steel superstructure, to measure both the modal response from acceleration measurements as well as quasi‐static and dynamic strain response. Ambient and forced loading conditions were applied to measure the response. Data results were compared over two separate periods approximately nine months apart.

The first eight mode shapes were produced from output‐only system identification providing natural frequencies ranging from approximately 6 to 42 Hz. The strain response was monitored over two different testing periods to measure various performance characteristics. Neutral axis, distribution factor, impact factor and end fixity were determined. Results appeared to be different over the two testing periods. They indicate that the load rating of the superstructure decreased over the nine month period, possibly due to deterioration of the materials or composite action.

The results from the two testing periods indicate that further testing needs to be completed to validate the change in response. It is difficult to say with certainty that the significant change in response is due to bridge deterioration and not other factors such as temperature effects on load rating. The sensor system, however, proved to provide high quality data and responses indicating its successful deployment for load testing and monitoring of highway infrastructure.

The paper provides a depiction of the change in structural behavior of a bridge superstructure using a wireless sensor system. The wireless system provided high‐rate data transmission in real time. Load testing at two different points in time, eight months apart, showed a significant change in bridge behavior. The paper provides a practical and actual physical load test and rating during these two periods for quantifiable change in response. It is shown that the wireless system is capable of infrastructure monitoring and that possible deterioration is expected with this particular bridge design. Additionally, the load testing occurred during different seasons, which could create cause for temperature effects in load rating. This can provide a basis for future performance monitoring techniques and structural health monitoring.

Transportation infrastructure asset management has long been an active but challenging problem for agencies, which urges to maintain a good state of their assets but faces budgetary limitations. Managing a network of transportation infrastructure assets, especially when the number is large, is a multifaceted challenge. This paper aims to develop a life-cycle cost analysis (LCCA) based transportation infrastructure asset management analytical framework to study the impacts of a few key parameters/factors on deterioration and life-cycle cost. Using the bridge as an example infrastructure type, the framework incorporates an optimization model for optimizing maintenance, repair, rehabilitation (MR&R) and replacement decisions in a finite planning horizon.

The analytical framework is further developed through a series of model variations, scenario and sensitivity analysis, simulation processes and numerical experiments to show the impacts of various parameters/factors and draw managerial insights. One notable analysis is to explicitly model the epistemic uncertainties of infrastructure deterioration models, which have been overlooked in previous research. The proposed methodology can be adapted to different types of assets for solving general asset management and capital planning problems.

The experiments and case studies revealed several findings. First, the authors showed the importance of the deterioration model parameter (i.e. Markov transition probability). Inaccurate information of p will lead to suboptimal solutions and results in excessive total cost. Second, both agency cost and user cost of a single facility will have significant impacts on the system cost and correlation between them also influences the system cost. Third, the optimal budget can be found and the system cost is tolerant to budge variations within a certain range. Four, the model minimizes the total cost by optimizing the allocation of funds to bridges weighing the trade-off between user and agency costs.

On the path forward to develop the next generation of bridge management systems methodologies, the authors make an exploration of incorporating the epistemic uncertainties of the stochastic deterioration models into bridge MR&R capital planning and decision-making. The authors propose an optimization approach that does not only incorporate the inherent stochasticity of bridge deterioration but also considers the epistemic uncertainties and variances of the model parameters of Markovian transition probabilities due to data errors or modeling processes.