Assessing the Condition of Reinforced Concrete Bridge Using Visual Inspection Ratings

The evolution of the state of a structure is characterized by deterioration. This is mainly due to corrosion of the steel reinforcement and damage from mechanical solicitations. The maintenance of existing infrastructures involves a good grasp of their condition and a high level of expertise on the part of the project managers. An accurate assessment of the bridge state condition is required to plan maintenance and repair activities for better durability, and to maintain the level of service of the road network. In this paper, an effective management framework for bridge is proposed using field observations from visual inspections. Each element of the bridge was evaluated separately by a visual inspection from which were derived ratings to quantify the structural performance and the material condition. The element ratings were also combined to obtain an overall rating for the bridge considering its defects and impact on the behavior of the complete structure. The modelling approach proposed in this work can better represent the deterioration of concrete-built bridges when the defect is visible. A representative structure in Quebec was studied to illustrate how to apply the methodology for the assessment of a real structure condition at specific times

Analysing estimation methods for the value of travel time from stated-preference surveys

The value of travel time (VOTT) is one of the key components for the transportation benefit evaluations. It is an imperative element in appraising the time saving benefits from transportation improvement projects and an essential input for travel demand forecast models. Furthermore, the welfare evaluation of transport pricing schemes is directly determined by VOTT estimates. After decades of research, the VOTT estimation is still a complicated task, and a research gap exists in terms of the development of an effective approach to estimate VOTT accurately. Our knowledge is limited in terms of a detailed comparison among different approaches to estimate VOTTs. This study examines two common methods to derive VOTTs from a stated-preference survey: contingent valuation (CV) and discrete choice modeling (DCM). To explore the impacts of using these two methods on VOTT estimates, the same data samples are employed from an online survey conducted in the Dallas-Fort Worth metroplex. For the CV method, the ordinal logistic regression is performed to estimate the expected willingness to pay given hypothetical time saving levels. For the DCM method, multinomial logistic regression models are developed to estimate the utility functions that determine the relative importance of travel time and travel cost and thus estimate VOTT. Furthermore, this thesis examines the traveler characteristics that affect VOTT by incorporating gender, age, income, and trip lengths in regression models. The results suggest that even if the data source (respondents) is the same, the two methods could result in different and even conflicting estimates. The CV method estimates an average VOTT of $6.10 per hour, substantially lower than the average estimate of$22.65 per hour using the DCM method. Generally, the DCM VOTT estimates are closer to calculated practical VOTTs (based on revealed preference data) and seem more reliable. The reason is that when asking respondents directly (CV), they generally hide their true willingness to pay, which results in lower VOTT estimates than those of DCM (with hypothetical scenarios). Furthermore, the two methods provide conflicting estimates when the effects of socio-demographics and travel characteristics are considered. This study sheds light on such discrepancies among methodologies to estimate VOTT. Finally, this study provides evidence that current project evaluation practices using a single method to estimate VOTT are biased/inaccurate, considering the potential inconsistencies among the estimation methods. Key words: value of travel time, contingent valuation, discrete choice modelling, willingness to pay, stated-preference surveys.La valeur du temps de parcours est l'une des composantes clé pour l'évaluation des avantages du transport. Il s'agit d'un élément impératif dans l'évaluation des gains de temps des projets d'amélioration de transports et une contribution essentielle aux modèles de prévision de la demande de transport. En outre, l'évaluation du bien-être des systèmes de tarification des transports est directement déterminée par les estimations de la valeur du temps de parcours. Après des décennies de recherche, l'estimation de la valeur du temps de parcours est encore une tâche compliquée, et un écart de recherche existe pour ce qui est du développement d'une approche efficace pour estimer la valeur du temps de déplacement avec précision. Nos connaissances sont limitées en ce qui concerne la comparaison détaillée entre différentes approches pour estimer les valeurs du temps de parcours. Cette étude examine deux méthodes courantes pour calculer les valeurs du temps de parcours à partir d'une enquête sur les préférences déclarées: l'évaluation contingente et la modélisation des choix discrets. Pour explorer les impacts de l'utilisation de ces deux méthodes sur les estimations de la valeur du temps de parcours, les mêmes échantillons de données sont utilisés à partir d'un sondage en ligne mené dans le Dallas-Fort Worth metroplex. Pour la méthode de l'évaluation contingente, la régression logistique ordinale est effectuée pour estimer la volonté de paiement attendue, compte tenu des niveaux hypothétiques d'économie de temps. Pour la méthode de la modélisation des choix discrets, des modèles de régression logistique multinomiale sont développés pour estimer les fonctions d'utilité qui déterminent l'importance relative du temps de déplacement et du coût du voyagement et donc estimer la valeur du temps de parcours. En outre, cette thèse examine les caractéristiques des voyageurs qui affectent la valeur du temps de parcours en intégrant le sexe, l'âge, le revenu et la durée des voyages dans les modèles de régression. Les résultats suggèrent que même si la source de données (répondants) est la même, les deux méthodes pourraient aboutir à des estimations différentes et même contradictoires. La méthode de l'évaluation contingente estime la valeur du temps de parcours moyen à 6,10 $l'heure, ce qui est nettement inférieur à l'estimation moyenne de 22,65$ l'heure selon la méthode de la modélisation des choix discrets. Généralement, les estimations de la modélisation des choix discrets sont plus proches des pratiques calculées (basées sur les données de préférence révélées) et semblent plus fiables. La raison en est qu'en demandant directement aux répondants (l'évaluation contingente), ils cachent généralement leur volonté réelle de payer, ce qui se traduit par des estimations inférieures à celles de la modélisation des choix discrets (avec des scénarios hypothétiques). De plus, les deux méthodes fournissent des estimations contradictoires lorsque l'on considère les effets de la socio-démographie et des caractéristiques de voyage. Cette étude met en lumière de telles divergences entre les deux méthodologies pour estimer la valeur du temps de parcours. Enfin, cette étude fournit la preuve que les pratiques actuelles d'évaluation de projet utilisant une seule méthode pour estimer la valeur du temps de parcours sont biaisées / inexactes, compte tenu des incohérences observées entre les méthodes et même les spécifications d'un même modèle.Mots clés: la valeur du temps de parcours, l'évaluation contingente, la modélisation des choix discrets, les sondages sur les préférences déclarée

Assessing Critical Road Sections: A Decision Matrix Approach Considering Safety and Pavement Condition

Identifying critical road sections that require prompt attention is essential for road agencies to prioritize monitoring, maintenance, and rehabilitation efforts and improve overall road conditions and safety. This study suggests a decision matrix with a hierarchical structure that factors in the pavement deterioration rate, infrastructure safety, and crash history to identify these sections. A Markov mixed hazard model was used to assess each section’s deterioration rate. The safety of the road sections was rated with the International Road Assessment Program star rating protocol considering all road users. Early detection of sections with fast deterioration and poor safety conditions allows for preventive measures to be taken and to reduce further deterioration and traffic crashes. Additionally, including crash history data in the decision matrix helps to understand the possible causes of a crash and is useful in developing safety policies. The proposed method is demonstrated using data from 4725 road sections, each 100 m, in Addis Ababa, Ethiopia. The case study results show that the proposed decision matrix can effectively identify critical road sections which need close attention and immediate action. As a result, the proposed method can assist road agencies in prioritizing inspections, maintenance, and rehabilitation decisions and effectively allocate budgets and resources

Assessing the condition state of a concrete bridge combining visual inspection and nonlinear deterioration model

The degradation of a concrete structure in northern climate is mainly due to the corrosion of steel reinforcements and cumulative damages from mechanical loading. Infrastructure managers heavily rely on ratings obtained from visual field surveys and the interpretation of inspection reports to predict the future structure states and to plan appropriate maintenance and replacement activities. In this paper, an effective structure management framework is proposed, combining information from on-site visual inspections and predictions from a nonlinear chloride transport model, to improve diagnostics for preventive maintenance. Meaningful predictions were obtained by using climatic data from neighboring weather stations and characterising the concrete transport properties with non-destructive tests. The chloride profiles from the model can be validated with core-drilled samples, if available. Predictions from the model were used to estimate the probability of the corrosion initiation and the condition states of the structural elements to complement the visual inspection observations. Finally, the ratings of each element were combined to obtain a global rating of the structure by considering the relative criticality of each element for the safety and the performance of the structure. The methodology was applied to a typical bridge in Montreal and demonstrated good agreement between the model predictions