scholarly journals Controversial aspects in seismic assessment and retrofit of structures in modern times

2006 ◽  
Vol 39 (2) ◽  
pp. 120-133 ◽  
Author(s):  
Stefano Pampanin
Keyword(s):  
Future Generation ◽  
Seismic Retrofit ◽  
Advanced Materials ◽  
Controversial Issues ◽  
Reinforced Polymers ◽  
Practical Applications ◽  
Current Trends ◽  
Supplemental Damping ◽  
Complex Decision Making ◽  
Complex Decision

Several alternative seismic retrofit and strengthening solutions have been studied in the past and adopted in practical applications ranging from conventional techniques, utilizing braces, jacketing or infills, to more recent approaches such as supplemental damping devices or advanced materials (e.g. Fibre Reinforced Polymers, FRP, or Shape Memory Alloys, SMA). A series of controversial issues are implicit in the complex decision-making process of seismic retrofit, where both rational and counter-intuitive solutions can satisfy some of the most critical aspects of multi-level performance-based seismic retrofit criteria. Interesting and fascinating suggestions and lessons can be obtained by reviewing the current trends in new design (i.e. innovative solutions for the future generation of buildings systems) as well as the architectural solutions used by the ancients. While walking this “bridge of knowledge” of our cultural heritage with the critical eyes of a curious and passionate observer, we can observe surprising commonality in engineering problems and their successful (and recently attempted) solutions. Understanding and implementing this heritage could lead to a uniquely stable platform for major breakthroughs in the development of “new solutions” in seismic design and retrofit.

Decision Filed Theory

2019 ◽  
pp. 640-654
Author(s):  
Lan Shao ◽  
Jouni Markkula
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Dft Theory ◽  
Practical Applications ◽  
Basic Model ◽  
Human Decision ◽  
Complex Decision Making ◽  
Ict System ◽  
Complex Decision ◽  
Decision Making Behavior

Human decision-making theories and formal models are increasingly used for developing advanced ICT-based intelligent systems and services. Decision filed theory (DFT) is one of the decision-making theories that has significant potential for practical applications in real-world decision-making situations. Successful empirical studied have shown that DFT theory is able to explain human decision-making behavior in real situations, and the model can be applied as a basis for ICT system and service design. In this chapter, the authors present the results of a systematic literature review conducted for analyzing and synthesizing the evidence of DFT development and its validated usage in different application areas. The results show that the interest in DFT and its applications has grown strongly during the last years. The basic model has been extended to cover more complex decision-making situations and its applications have been widening.

Decision Filed Theory

2018 ◽  
pp. 2108-2120
Author(s):  
Lan Shao ◽  
Jouni Markkula
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Intelligent Systems ◽  
Dft Theory ◽  
Practical Applications ◽  
Basic Model ◽  
Human Decision ◽  
Complex Decision Making ◽  
Ict System ◽  
Complex Decision

Human decision making theories and formal models are increasingly used for developing advanced ICT based intelligent systems and services. Decision Filed Theory (DFT) is one of the decision making theories that has significant potential for practical applications in real-world decision making situations. Successful empirical studied have shown that DFT theory is able to explain human decision making behaviour in real situations and the model can be applied as a basis for ICT system and service design. In this article, we present the results of a Systematic Literature Review that we conducted for analysing and synthesizing the evidence of DFT development and its validated usage in different application areas. The results show that the interest in DFT and its applications has grown strongly during the last years. The basic model has been extended to cover more complex decision making situations and its applications have been widening.

scholarly journals A Delphi Study Investigating Clinicians’ Views on Access to, Delivery of, and Adaptations of MBCT in the UK Clinical Settings

Mindfulness ◽  
2021 ◽  
Author(s):  
Kate Williams ◽  
Samantha Hartley ◽  
Peter Taylor
Keyword(s):  
Delphi Study ◽  
Good Practice ◽  
Evidence Base ◽  
Clinical Settings ◽  
Training Support ◽  
Decision Making Processes ◽  
Complex Decision Making ◽  
Complex Decision ◽  
The Uk ◽  
Clinical Populations

Abstract Objectives Mindfulness-based cognitive therapy (MBCT) is a well-evidenced relapse-prevention intervention for depression with a growing evidence-base for use in other clinical populations. The UK initiatives have outlined plans for increasing access to MBCT in clinical settings, although evidence suggests that access remains limited. Given the increased popularity and access to MBCT, there may be deviations from the evidence-base and potential risks of harm. We aimed to understand what clinicians believe should be best clinical practice regarding access to, delivery of, and adaptations to MBCT. Methods We employed a two-stage Delphi methodology. First, to develop statements around best practices, we consulted five mindfulness-based experts and reviewed the literature. Second, a total of 59 statements were taken forward into three survey rating rounds. Results Twenty-nine clinicians completed round one, with 25 subsequently completing both rounds two and three. Forty-four statements reached consensus; 15 statements did not. Clinicians agreed with statements regarding sufficient preparation for accessing MBCT, adherence to the evidence-base and good practice guidelines, consideration of risks, sufficient access to training, support, and resources within services, and carefully considered adaptations. The consensus was not reached on statements which reflected a lack of evidence-base for specific clinical populations or the complex decision-making processes involved in delivering and making adaptations to MBCT. Conclusions Our findings highlight the delicate balance of maintaining a client-centred and transparent approach whilst adhering to the evidence-base in clinical decisions around access to, delivery of, and adaptations in MBCT and have important wide-reaching implications.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

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