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Although ground penetrating radar (GPR) technology is commonly used to assess the condition of reinforced-concrete (RC) bridge decks, the GPR data interpretation is not straightforward. Further, the thresholds that define the severity of deterioration are selected arbitrarily. This paper aims to solve a problem associated with GPR results generated by using a numerical amplitude method to assess corrosiveness of bridge decks.

Data, for more than 50 different bridge decks, were collected using a ground-coupled antenna. Depth-correction was performed for the collected data to normalize the reflected amplitude. Using k-means clustering technique, the amplitude values of each bridge deck were classified into four categories. Later, statistical analysis was performed where the threshold values of different categories of corrosion and deterioration are chosen. Monte-Carlo simulation technique was used to validate the value of these thresholds. Moreover, a sensitivity analysis was performed to realize the effect of changing the thresholds in the areas of corrosion.

The final result of this research is a four-category (good, fair, poor and critical) GPR scale with three fixed numerical thresholds (−7.71 dB, −10.04 dB and −14.63 dB) that define these categories. Besides, deterioration curves have been modeled using Weibull function and based on GPR outputs and corrosion areas.

The developed numerical GPR-based scale and deterioration models are expected to help the decision-makers in assessing the corrosiveness of bridge decks accurately and objectively. Hence, they will be able to take the right intervention decision for managing these decks.

Parameter identification is a technique which aims at determining material or other process parameters based on a combination of experimental and numerical techniques. In recent years, heuristic approaches, such as genetic algorithms (GAs), have been proposed as possible alternatives to classical identification procedures. The present work shows that particle swarm optimization (PSO), as an example of such methods, is also appropriate to identification of inelastic parameters. The paper aims to discuss these issues.

PSO is a class of swarm intelligence algorithms which attempts to reproduce the social behaviour of a generic population. In parameter identification, each individual particle is associated to hyper-coordinates in the search space, corresponding to a set of material parameters, upon which velocity operators with random components are applied, leading the particles to cluster together at convergence.

PSO has proved to be a viable alternative to identification of inelastic parameters owing to its robustness (achieving the global minimum with high tolerance for variations of the population size and control parameters), and, contrasting to GAs, higher convergence rate and small number of control variables.

PSO has been mostly applied to electrical and industrial engineering. This paper extends the field of application of the method to identification of inelastic material parameters.

The purpose of this paper is to solve numerically the identification of the thermal conductivity of an inhomogeneous and possibly anisotropic medium from interior/internal temperature measurements.

The formulated coefficient identification problem is inverse and ill-posed, and therefore, to obtain a stable solution, a non-linear regularized least-squares approach is used. For the numerical discretization of the orthotropic heat equation, the finite-difference method is applied, while the non-linear minimization is performed using the MATLAB toolbox routine lsqnonlin.

Numerical results show the accuracy and stability of solution even in the presence of noise (modelling inexact measurements) in the input temperature data.

The mathematical formulation uses temporal temperature measurements taken at many points inside the sample, and this may be too much information that is provided to identify a space-wise dependent only conductivity tensor.

As noisy data are inverted, the paper models real situations in which practical temperature measurements recorded using thermocouples are inherently contaminated with random noise.

The identification of the conductivity of inhomogeneous and orthotropic media will be of great interest to the inverse problems community with applications in geophysics, groundwater flow and heat transfer.

The current investigation advances the field of coefficient identification problems by generalizing the conductivity to be anisotropic in addition of being heterogeneous. The originality lies in performing, for the first time, numerical simulations of inversion to find the orthotropic and inhomogeneous thermal conductivity from noisy temperature measurements. Further value and physical significance are brought in by determining the degree of cure in a resin transfer molding process, in addition to obtaining the inhomogeneous thermal conductivity of the tested material.

The purpose of this paper is to investigate the transient three‐dimensional temperature distribution for a laser sintered duraform fine polyamide part by a moving Gaussian laser beam. The primary objective of the present paper is to develop computationally efficient numerical simulation technique with the commercially available finite element software domain for the accurate prediction of the temperature history and heat‐affected zones of the laser sintered parts so as to finally obtain the density of the sintered sample.

The paper proposes a mathematical model of scanning by moving laser beam and sintering sub‐model. Based on the mathematical models, a simulation model was developed by using author written subroutines in ANSYS® 11.0, a general purpose finite element software. The simulation model was then run at experimental designed points using two‐level factorial design of experiments (DOE) approach. The data thus generated were used to predict the equation for the density of sintered part in terms of process parameters using Design Expert software in order to analyse the designed experiments.

Laser power and scan spacing were found to be significant parameters affecting the part density. Amongst the interaction terms, significant effect of laser power was found on the part density at the lower settings of the scan velocity. Temperature‐time plots were generated to study the transient temperature distribution for the sintering process and with further applicability to study the thermal stresses.

The simulation model hence developed can be used for only simple part geometries and cannot be generalised for any complex geometry.

The paper presents a simulation model which is integrated with a DOE approach so as to develop a robust as well as simple and fast approach for the optimization of quality objective.

The purpose of this study is to developed a simplified thermo-fluid model of an engine cowling in a small airplane. An aircraft engine system is composed of different elements operating at various temperatures and in conjunction with the composite nacelle creates a region with high intensity of heat transfer to be covered by the cooling/ventilation systems. Therefore a thermal analysis, accounting for the complex heat transfer modes, is necessary in order to verify that an adequate cooling is ensured and that temperatures of the nacelle are maintained within the operating limits throughout the whole aircraft's flight.

Simplified numerical simulations of conductive, convective and radiative heat transfer in the engine bay of the small airplane I-23 in a tractor arrangement were performed for different air inlet and outlet configurations and for varying conditions existing in air inlets during the flight. The model is based on the control volume approach for heat and fluid flow as well as for thermal radiation and on k-ɛ turbulence model.

The flow and temperature distributions inside the cowling were determined, and high-temperature spots on the internal side of the nacelle and on other airplane systems located close to the turboprop engine and the exhaust system were found. The thermal radiation was found to play the key role in heat transfer inside the engine bay. The optimal configuration of air inlets and outlets was determined.

The obtained results will help in future studies on ventilation and cooling systems and will contribute to the selection of materials for parts of the engine bay and the nacelle as well as in developing solutions for reducing the temperature inside the cowling of the airplane I-23.

A complete simplified thermo-fluid model of heat transfer inside the engine bay of the airplane I-23 was developed. Additionally, influence of the thermal radiation on temperature distribution at the nacelle was investigated.

The purpose of this paper is to present a fault tree (FT)-based approach for quantitative risk analysis in the construction industry that can take into account both objective and subjective uncertainties.

In this research, the identified basic events (BEs) are first categorized based on the availability of historical data into probabilistic and possibilistic. The probabilistic and possibilistic events are represented by probability distributions and fuzzy numbers, respectively. Hybrid uncertainty analysis is then performed through a combination of Monte Carlo simulation and fuzzy set theory. The probability of occurrence of the top event is finally calculated using the proposed FT-based hybrid uncertainty analysis method.

The efficiency of the proposed method is demonstrated by implementing in a real steel structure project. A quantitative risk assessment is performed for weld cracks, taking into account of both types of uncertainties. An importance analysis is finally performed to evaluate the contribution of each BE to the probability of occurrence of weld cracks and adopt appropriate response strategies.

In this research, the impact of objective (aleatory) dependence between the occurrences of different BEs and subjective (epistemic) dependence between estimates of the epistemically uncertain probabilities of some BEs are not considered. Moreover, there exist limitations to the application of fuzzy set rules, which were used for aggregating experts’ opinions and ranking purposes of the BEs in the FT model. These limitations can be investigated through further research.

It is believed that the proposed hybrid uncertainty analysis method presents a robust and powerful tool for quantitative risk analysis, as both types of uncertainties are taken into account appropriately.

Gives introductory remarks about chapter 1 of this group of 31 papers, from ISEF 1999 Proceedings, in the methodologies for field analysis, in the electromagnetic community. Observes that computer package implementation theory contributes to clarification. Discusses the areas covered by some of the papers ‐ such as artificial intelligence using fuzzy logic. Includes applications such as permanent magnets and looks at eddy current problems. States the finite element method is currently the most popular method used for field computation. Closes by pointing out the amalgam of topics.