Compensating the lack of big data in construction industry with expert knowledge: a case study

Due to various reasons, there is a lack of big data in the construction industry, one of the main obstacles to a broader implementation of AI. Another obstacle is adhering to analytical methods in fields more suitable for AI solutions. If appropriately used, multidisciplinary expert knowledge can compensate for these problems and enhance the application of AI techniques in construction. The case study refers to rapid earthquake loss assessment. The problem with traditional systems is their low accuracy, making them unreliable and unusable in the recovery process, which is the purpose of loss assessment systems. Low accuracy is caused by too much uncertainty in analytical and insufficient data sets to create vulnerability curves in empirical methods. The contribution of this research is designing a new kind of rapid earthquake loss assessment system using multidisciplinary expert knowledge and AI methods. The problem of small data sets was solved using the procedure of representative sampling, which makes a small sample informative and sufficient to use. The low accuracy of analytical methods is caused by assuming theoretical vulnerability relations before an earthquake. The new approach uses trained assessors to perform on-the-ground observation of actual damage on the representative sample after an earthquake. AI methods are then used to predict damage to the remaining building portfolio, which is more accurate and still rapid enough. Another contribution is using a building representation without earthquake data which eliminates the need for analytical methods, shake maps and robust ground motion sensor networks, making the proposed framework unique and applicable in any regio

Sensibilité des observables radars à la variabilité temporelle et à la configuration géométrique de forêts tempérées et tropicales à partir de mesure de proximité haute-résolution

L'augmentation importante de la population mondiale, et par conséquent de ses besoins, exerce une pression de plus en plus importante sur les surfaces forestières. L'outil le mieux adapté au suivi des forêts, à l'échelle du globe, est la télédétection. C'est dans ce contexte que se situe ce travail de thèse, qui vise à améliorer l'estimation des paramètres biophysiques des arbres à partir de données de télédétection. L'originalité de ce travail a été d'étudier cette estimation des paramètres biophysiques en menant plusieurs études de sensibilité avec une démarche expérimentale sur des données expérimentales et sur des données simulées. Tout d'abord, l'étude s'est portée sur des séries temporelles de mesures de diffusiométrie radar obtenues sur deux sites : l'un constitué d'un cèdre en zone tempérée et l'autre d'une parcelle de forêt tropicale. Puis, cette étude de sensibilité a été poursuivie en imageant, avec une résolution élevée, plusieurs parcelles aux configurations différentes à l'intérieur d'une forêt de pin. Enfin, des données optiques et radars simulées ont été fusionnés afin d'évaluer l'apport de la fusion de données optique et radar dans l'inversion des paramètres biophysiques.The significant increase of the world population, and therefore its needs, pushes increasingly high in forest areas. The best tool for monitoring forest across the globe is remote sensing. It is in this context that this thesis, which aims to improve the retrieval of biophysical parameters of trees from remote sensing data, takes place. The originality of this work was to study the estimation of biophysical parameters across multiple sensitivity studies on experimental data and simulated data. First, the study focused on the time series of radar scatterometry measurements obtained on two sites: one characterized by a cedar in the temperate zone and the other by a forest plot of rainforest. Then, the sensitivity analysis was continued by imaging with high resolution, several forest plots with different configurations within a pine forest. Finally, simulated radar and optical data were combined to evaluate the contribution of optical and radar data fusion in the inversion of biophysical parameters.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF