A causal modelling framework for the simulation and explanation of the behaviour of structures

doi:10.1016/S0954-1810(96)00040-4

Artificial Intelligence in Engineering

Volume 11, Issue 3, July 1997, Pages 205-216

https://doi.org/10.1016/S0954-1810(96)00040-4 Get rights and content

Abstract

An approach to the modelling of systems in civil engineering is presented. It allows the integration of quantitative relations in a qualitative causal framework which uses objects and Petri nets to represent the device and process ontologies. This approach supports the modelling and simulation of the behaviour of a physical system and causal explanations of it. The explanations are customisable depending on the needs of different users. The approach is shown by modelling the seismic behaviour of a masonry building, simulating it and generating causal explanations tailored for the needs of different users. An example application is presented through IGOR, a decision support system for seismic assessment of buildings and planning of precautionary operations.

References (25)

K.D. Forbus
Qualitative physics: past, present and future
K.D. Forbus
Qualitative process theory
Artificial Intelligence
(1984)
M.R. Wick et al.
Re-constructive expert systems explanation
Artificial Intelligence
(1992)
D.S. Weld et al.
Qualitative physics: a personal view
D.S. Weld et al.
Causal explanations of behaviour
P.A. Fishwick et al.
A multimodel methodology for qualitative model engineering
ACM Transactions on Modelling and Computer Simulation
(1992)
G.H. Miyasato et al.
Seismic risk analysis system
M. Ishizuka et al.
SPERIL I — Computer based structural damage assessment system
T. Pagnoni et al.
AMADEUS: a KBS for the assessment of earthquake damaged buildings
X.J. Zhang et al.
Automation of knowledge organization and acquisition
Microcomp. Civ. Engng.
(1988)

L.M. Bozzo et al.

Qualitative reasoning about seismic behavior of buildings

C.A. Petri

General net theory

Cited by (11)

Developing intelligent tensegrity structures with stochastic search
2002, Advanced Engineering Informatics
Since structural control of civil structures was first proposed in 1972, most research and applications have focused on enhancing the safety of structures under extreme conditions. This paper introduces a new direction in structural control: the use of computational methods and explicitly defined knowledge to improve serviceability and maintenance of civil structures. The objectives of such structures, called intelligent structures, are to maintain and improve structural performance by recognizing changes in behaviors and loads, adapting the structural geometry to meet defined goals, and using past events to improve future performance. A computational framework based on intelligent control methodology is presented that combines reasoning from explicit knowledge, search, learning and planning to illustrate a vision for intelligently controlled civil structures. This is applied to enable global shape control of an adjustable tensegrity structure using a combination of simulated annealing search, dynamic relaxation analysis and structural measurements. Active tensegrity structures are modular, reusable cable structures that do not require expensive anchorages or steep slopes. This provides an innovative solution for temporary structures. Particularly, when used for exhibitions, they could become part of an exhibition, rather than just house one. As part of a multi-stage project, results presented in this paper demonstrate the feasibility of building an intelligent tensegrity structure.
Causal Graphical Models for Systems-Level Engineering Assessment
2021, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Software quality evaluation: State of the art, practice, and directions
2015, Systems Analysis and Design: People, Processes, and Projects
A model for earthquake risk management based on the life-cycle performance of structures
2011, Civil Engineering and Environmental Systems
OEQS-based modelling and simulation of employee group behaviors
2009, Proceedings of the IASTED International Conference on Modelling, Simulation, and Identification, MSI 2009
Qualitative simulation of teachers group behaviors based on BP neural network
2009, Communications in Computer and Information Science

View all citing articles on Scopus

View full text

A causal modelling framework for the simulation and explanation of the behaviour of structures

Abstract

Artificial Intelligence

Artificial Intelligence

Qualitative physics: a personal view

Causal explanations of behaviour

A multimodel methodology for qualitative model engineering

ACM Transactions on Modelling and Computer Simulation

Seismic risk analysis system

SPERIL I — Computer based structural damage assessment system

AMADEUS: a KBS for the assessment of earthquake damaged buildings

Automation of knowledge organization and acquisition

Microcomp. Civ. Engng.

Qualitative reasoning about seismic behavior of buildings

General net theory