The current design code governing bridge structure resistance to vessel impact loads in the U.S. is the American Association of State Highway and Transportation Officials' (AASHTO) Guide Specification and Commentary for Vessel Collision Design of Highway Bridges. The code stipulated method, based on Meir-Dornberg's equivalent static load method, is usually not warranted because of insufficient data on the impact load histories and wide scatter of the impact force values. The AASHTO load equations ignore certain fundamental factors that affect the determination of impact forces and bridge dynamic responses. Some examples of factors that are omitted during standard impact force analysis are: impact duration, pier geometry, barge-barge and barge-pier interactions, and structural characteristics of bridges. The purpose of this research is to develop new methods and models for predicting barge impact forces on piers. In order to generate research information and produce more realistic flotilla impact data, extensive finite element simulations are conducted. A set of regression formulas to calculate the impact force and time duration are derived from the simulation results. Also, a parametric study is performed systematically to reveal the dynamic features of barge-bridge collisions. A method to determine the quasi upper bound of the average impact force under any given scenarios is preposed. Based on the upper bounds of the average impact forces, an impact spectrum procedure to determine the dynamic response of piers is developed. These analytical techniques transform the complex dynamics of barge-pier impact into simple problems that can be solved through hand calculations or design charts. Furthermore, the dependency of the impact forces on barge-barge and barge-pier interactions are discussed in detail. An elastoplastic model for the analysis of multi-barge flotillas impacting on bridge piers is presented. The barge flotilla impact model generates impact force time-histories for various simulation cases in a matter of minutes. The results from the proposed model are compatible with the respective impact time-histories produced by an exhuaustive finite element simulation. All of the proposed methods and loading functions in this study are illustrated through design examples. Accordingly, the research results may help engineers to enhance bridge resistance to barge impacts and also lead to economic savings in bridge protection design.

Le trafic maritime est le principal contributeur des bruits sous-marins anthropique : depuis les années 1970, l’augmentation du trafic maritime hauturier a provoqué dans certaines zones une augmentation du bruit ambiant de plus de 10 dB. En réponse à cette préoccupation, la Directive Cadre pour la Stratégie pour le Milieu Marin (DCSMM) recommande un suivi acoustique. Peu d’études s’intéressent à l’activité côtière et aux bruits rayonnés par les petites embarcations ainsi qu’à leurs conséquences sur la faune marine alors que ces environnements côtiers sont les pourvoyeurs de 41.7 % des services écosystémiques produits par les océans.A mi-chemin entre le monde académique et le monde industriel, le travail présenté aux différents questions scientifiques et industrielles sur la thématique du trafic côtier, en termes de l’étude de son influence dans le paysage acoustique et de capacité à détecter et classifier les embarcations côtières.En l’absence d’information sur le trafic maritime côtier, un protocole d’identification visuelle par traitement d’images GoPro® produisant les mêmes données que l’AIS (position, vitesse, taille et type d’embarcation) est proposé et permet la création de carte du trafic maritime sur un disque de 1.6km de rayon. D’un point de vue acoustique, le trafic est caractérisé par deux descripteurs acoustiques, le SPL lié à la distance du bateau le plus proche et l’ANL caractérisant le nombre de bateaux dans un disque de 500 m de rayon. Le suivi spatio-temporel de ces descripteurs permet d’identifier l’impact du trafic maritime dans le paysage acoustique des environnements côtiers. La détection et la classification sont réalisées après caractérisation individuelle du bruit par un ensemble de paramètres acoustiques et par l’utilisation d’algorithmes d’apprentissage supervisé. Un protocole spécifique pour la création de l’arborescence de classification est proposé par comparaison des données acoustiques aux caractéristiques physiques et contextuelle de chaque bateau.Les travaux présentés sont illustrés sur la flottille d’embarcations côtières présente dans la baie de Calvi (Corse) durant la saison estivale. / Marine traffic is the main contributor to anthropogenic underwater noise: since the 1970s, the increase in deep-sea shipping has increased the ambient noise by more than 10 dB in some areas. In response to this concern, the Marine Strategy Framework Directive (MSFD) recommends acoustic monitoring. Few studies are concerned with coastal activity and the noises radiated by small craft while these coastal environments are the purveyors of 41.7% of the ecosystem services produced by the oceans.Between the academic and the industrial world, this PhD was to answer the different scientific and industrial questions on the topic of the coastal traffic in terms of the influence in the soundscape and the detection and classification of the coastal craft.Without information on the coastal maritime traffic, a visual identification protocol is proposed using GoPro® images processing and produced the same data as the AIS (position, speed, size and type of craft); It allows to create maritime traffic maps on a disk of 1.6km radius. The traffic is characterized by two acoustic descriptors: the SPL linked to the distance of the nearest boat and the ANL linked to the number of boats present in a 500 m radius disc. The spatiotemporal monitoring of these descriptors allows to identify the impact on the maritime traffic on the coastal acoustic landscape. The acoustic detection and the classification are performed after individual characterization of the noise by a set of acoustic parameters and using of supervised machine learning algorithm. A specific protocol for the creation of the classification tree is proposed by comparing the acoustic data with the physical and contextual characteristics of each boat.The methods are applied on the flotilla of coastal boats present in the Bay of Calvi (Corsica) during summer.