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The stadium structures have unique structural features increasing the significance of structural monitoring systems specifically designed for them. Aside from vibrations serviceability concerns and human -induced excitations, the development and propagation of structural damage under all possible atmospheric and seismic conditions need to be closely monitored for structural resiliency and integrity of the stadia. As such, Structural Health Monitoring (SHM) methods combined with effective data evaluation methodologies need to be deployed to monitor the structural performance of stadiums. Even though stadia monitoring has been performed at multiple locations in the world, a web based and real-time SHM network of stadia is not known to authors. As a preliminary study for the network implementation of stadia monitoring with acceleration measurements, the presented work focuses on the fundamental steps to accomplish this goal, with a collaborative research effort between Qatar University, the University of Central Florida, and University of Alberta. The authors performed analytical investigations and experimental testing on stadium -type structures built in laboratory environments for the development of the SHM framework. Specialized signal processing algorithms, sensing suites and approaches considering multi -scale monitoring were used on collected acceleration measurements. The novelty of the work presented in this manuscript are the following items which exist simultaneously in the developed SHM framework. The developed framework is a web -based monitoring application where structural damage is detected in real-time. The proposed methodology operates directly on raw acceleration signals and runs at a network level. With that, the damage detection, damage localization, and damage quantification tasks are performed simultaneously, while the feature extraction and classification stages are combined in one learning body.

CB2 is a multi-agent platform to study collaborative natural language interaction in a grounded task-oriented scenario. It includes a 3D game environment, a backend server designed to serve trained models to human agents, and various tools and processes to enable scalable studies. We deploy CB2 at https://cb2.ai as a system demonstration with a learned instruction following model., Comment: ACL 2023 Demo paper

A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and language pretraining, we find that: across 7 architectures trained with 4 algorithms on massive datasets, they struggle at compositionality. To arrive at this conclusion, we introduce a new compositionality evaluation benchmark, CREPE, which measures two important aspects of compositionality identified by cognitive science literature: systematicity and productivity. To measure systematicity, CREPE consists of a test dataset containing over $370K$ image-text pairs and three different seen-unseen splits. The three splits are designed to test models trained on three popular training datasets: CC-12M, YFCC-15M, and LAION-400M. We also generate $325K$, $316K$, and $309K$ hard negative captions for a subset of the pairs. To test productivity, CREPE contains $17K$ image-text pairs with nine different complexities plus $183K$ hard negative captions with atomic, swapping and negation foils. The datasets are generated by repurposing the Visual Genome scene graphs and region descriptions and applying handcrafted templates and GPT-3. For systematicity, we find that model performance decreases consistently when novel compositions dominate the retrieval set, with Recall@1 dropping by up to $12\%$. For productivity, models' retrieval success decays as complexity increases, frequently nearing random chance at high complexity. These results hold regardless of model and training dataset size., Comment: Updated figures and numbers

Recent video question answering benchmarks indicate that state-of-the-art models struggle to answer compositional questions. However, it remains unclear which types of compositional reasoning cause models to mispredict. Furthermore, it is difficult to discern whether models arrive at answers using compositional reasoning or by leveraging data biases. In this paper, we develop a question decomposition engine that programmatically deconstructs a compositional question into a directed acyclic graph of sub-questions. The graph is designed such that each parent question is a composition of its children. We present AGQA-Decomp, a benchmark containing $2.3M$ question graphs, with an average of $11.49$ sub-questions per graph, and $4.55M$ total new sub-questions. Using question graphs, we evaluate three state-of-the-art models with a suite of novel compositional consistency metrics. We find that models either cannot reason correctly through most compositions or are reliant on incorrect reasoning to reach answers, frequently contradicting themselves or achieving high accuracies when failing at intermediate reasoning steps., Comment: To appear in CVPR 2022. 23 pages, 12 figures and 12 tables

Stadiums like those used for sporting or concert events are distinct from other civil engineering structures due to several different characteristics. Some challenges mainly originate from the interaction with the human factor, as stadiums are subjected to both synchronized and random motion of large crowds. The investigations in the literature on this topic clearly state that stadiums designs are in urgent need of more reliable load quantification and modeling strategies, deeper understanding of structural response, generation of simple but efficient human–structure interaction models, and more accurate criteria for vibration acceptability. Although many esthetically pleasing and technologically advanced stadiums have been designed and constructed using structurally innovative methods, recent research on this field still calls for less conservative and more realistic designs. This article aims to highlight the recent advances in this field and to provide a follow-up to the literature review covering until 2008 (Jones et al., 2011a) on vibration serviceability of stadiums structures. The article will also discuss new sensing and monitoring techniques on load-time history measurements and their regeneration, as well as crowd motion, stadium health monitoring, and human comfort analysis. Operational effects of crowds on the dynamic properties are also discussed. The article concludes with a forward look on the recommended work and research for dynamic assessment of stadiums.

Studies with large physical models are a vital link between the theoretical work and field applications provided that these models are designed to represent real structures where various types and levels of uncertainties can be incorporated. While comprehensive analytical and laboratory joint studies are ongoing at Qatar University, University of Central Florida and University of Alberta, this paper presents the initial findings of dynamic testing at Qatar University. A laboratory stadium structure (grandstand simulator) has been constructed at Qatar University. Capable of housing thirty spectators, Qatar University grandstand simulator is arguably the largest laboratory stadium in the world. The structure is designed in a way that several different structural configurations can be tested in laboratory conditions to enable researchers to test newly developed damage detection algorithms. The study presented in this paper covers the finite element modeling and modal testing of the test structure.

Unlike most of civil engineering structures whose static and dynamic responses are estimated accurately through several codes and guidance, stadiums reserve a distinctive place especially when it comes to their dynamic behavior. This difference takes its source from several factors such as influence of crowd size, motion and slenderness of the structure. The most noticeable form of this difference shows itself as excessive vibration levels which is actually a threat to the serviceability of these structures. Eventually, it becomes essential to carefully evaluate several steps of this particular problem starting from correct representation of crowd activity through accurate loadings and human-structure interaction models to arranging acceptable vibration serviceability limits. This publication intends to point out the newly developed techniques and discovered issues on several stages of the problem during the last decade.

One of the main tasks in structural health monitoring process is to create reliable algorithms that are capable of translating the measured response into meaningful information reflecting the actual condition of the monitored structure. The authors have recently introduced a novel unsupervised vibration-based damage detection algorithm that utilizes self-organizing maps to quantify structural damage and assess the overall condition of structures. Previously, this algorithm had been tested using the experimental data of Phase II Experimental Benchmark Problem of Structural Health Monitoring, introduced by the IASC (International Association for Structural Control) and ASCE (American Society of Civil Engineers). In this paper, the ability of this algorithm to quantify structural damage is tested analytically using an experimentally validated finite element model of a laboratory structure constructed at Qatar University.

BDK78 977-07, The maintenance costs related to movable bridges are considerably higher than those of fixed bridges, mostly, because of the complex interaction of the mechanical, electrical and structural components. A malfunction of any, component can cause an unexpected failure of bridge operation, which creates problems for both land and, maritime traffic. Maintenance processes associated with the operation system and mechanical parts require, special expertise. A comprehensive monitoring system was implemented on Sunrise Bridge (Ft. Lauderdale) to, track the behavior and condition of several critical mechanical, electrical and structural components. A number of, tests and monitoring of the bridge yielded a wide variety of data, which were analyzed in detail with methods, developed by the PI and his research team, and the results obtained at the end of the project were reported to, FDOT in a detailed report. After the completion of this previous project, the bridge was already scheduled for, painting; however, the monitoring system was significantly damaged during the preparation, sandblasting and, painting despite the considerable efforts of FDOT personnel to protect the system. This extension project, therefore focuses on repairing the monitoring system, which was affected by the painting operation, collecting and, analyzing more data and preparing the system for FDOT. First, details of the field work conducted to repair the, damaged monitoring system are presented. Then, analysis of data that were collected after the monitoring, system was repaired is presented for different components. The baseline response and the thresholds for, acceptable behavior were established. During this phase of the project, unanticipated behaviors were observed, for two components (one at the span locks and one at the gearbox) at two different times. These findings, indicating the unanticipated behavior using the monitoring system are also corroborated with the independent, maintenance reports. These changes in behavior required maintenance work at the span lock and gearbox as, given in the maintenance logs. Finally, recommendations are provided based on the findings and experiences, from this project.