Any occurrence of cracking in masonry structures needs the correct diagnosis of the cause to be made in order that suitable remedial action can be taken. In order to do this a wide range of expert knowledge is required by the investigating engineer. This paper describes the development and testing of an intelligent knowledge based system (IKBS) which will aid engineers carrying out these types of investigations. Particular emphasis is given in the paper to knowledge acquisition and the subsequent testing of the IKBS and these issues are addressed in some detail. The paper also highlights the importance of not only using knowledge to prove causes but also to disprove causes.The use of knowledge to disprove causes is shown to lead to shorter consultations and more accurate conclusions.

This paper describes the diagnosis phase of a highway safety expert system. The overall objective of the expert system is to provide highway safety officials with an efficient tool to identify accident prone locations and then quickly and reliably advise on the appropriate countermeasure(s) based on an analysis of the accident and roadway environment data. The system has three basic phases: detection, diagnosis and remedy. In the diagnosis phase a knowledge-based system is developed to identify the causes and the contribution factors of safety problems at accident prone locations and to suggest appropriate countermeasures. It is shown that the knowledge-based approach best-suits the diagnosis process since it involves a great deal of judgment and experience by the safety engineer. The paper describes different steps involved in developing the diagnosis phase including: knowledge acquisition, problem solving strategy, system features, uncertainty handling, and system verification and validation. The output of the diagnosis phase is a set of applicable countermeasures for each accident prone location and the degree of belief in each countermeasure. The knowledge-based system was validated using several case studies which demonstrated satisfactory results.

Nondestructive inspection tools used for pipeline inspection are uncertain in detecting corrosion pits and in sizing detected defects. Probability-based optimal inspection schedule analysis must taken these uncertainties into account. In this paper, the probability of time to failure is formulated as integral equations with domain of integration expressed as unions and intersections of domains of failure, defect detection, defect nondetection and maintenance criterion. The rate of defect detection as a function of defect size and the maintenance criterion are used as filters to eliminate the defects that are not fit for service in an expected remaining service life after inspection. Simulation procedure is given to estimate the probability distribution of time to failure by using the integral equations. To facilitate the probabilistic analysis, a standard uniformly distributed variate is introduce and used in defining the domain of detected defect and the domain of nondetected defect. The advantages of using the proposed simulation procedure are discussed. Optimal inspection schedules are selected based on the minimum value of the maximum probability of time to failure before inspections and before the time at the end of service life. Effect of inspection quality and maintenance criterion on probability of time to failure and on selecting optimal inspection schedule is presented through an illustrative application study.

A genetic algorithm (GA)-based model to deal with time-cost trade-off problems is presented. The traditional algorithms assume the unlimited availability of resources. Instead, the proposed model allows for resource constraints. Accordingly, the trade-off is considered in terms of the level of resources to be deployed for each activity. At the same time the model schedules the starts of the activities in order to optimize the objective function. The activity starts is a significant factor in the case of non-uniform available profile. The GA searches both spaces of resource utilization and activity starts to determine the optimal schedule that conforms to the resource available profile. Although, in principle, this has the potential of a combinatorial explosion, earlier work suggests that the GA-based model can be applied to larger networks without appearing to suffer from this problem. Allowing the project manager to think in terms of resource utilization makes this approach consistent with resource allocation problems, and more pragmatic and appealing. The model can also be used to solve conventional time-cost trade-off problems by a simple modification of the objective function. Numerical examples are used to illustrate the working of the model. The examples include a case of varying resource available profile.

The paper illustrates the potential of approximate reasoning and fuzzy logic in the evaluation of transport projects where projects are characterised in terms of multiple factors or characteristics. A method is illustrated which incorporates fragments of imprecise information (conditional propositions, implications). The antecedents in each fragment involve factors of environmental significance and the consequent is a measure of satisfaction associated with those factors. Factors may be either quantitative or qualitative.

An algorithm for on-line detection of damage to structures caused by ground shaking is presented. Real-time sampled response data are processed by templates in the form of ID (for structural identification) neural networks, which differentiate damage according to similarity of the response to those encapsulated in the templates. Numerical examples based on a simple 2-story steel-frame building are used to illustrate the proceedings and to underscore the limitations of the method. The challenges of on-line damage detection are discussed in detail to promote better understanding of how the proposed algorithm has evolved and, in particular, why neural networks are used. Widespread application of the algorithm, and damage detection in general, depends on the establishment of an adequate ID networks database, a far more daunting task in practice than in the theoretical setting of the paper.

A new definition of the damage degree of SFRC is introduced based on the investigation of damage phenomena of Steel Fibre Reinforced Concrete (SFRC) under axial compressive fatigue loads. The damage equation, which describes the damage evolution in the material, is derived based on the above definition and continuous damage mechanics. It can be found that the evolution model has a good agreement with the experimental results.

Abstract Level of service (LOS) is a qualitative measure of traffic flow from the users' perspectives. This paper presents a new methodology to find LOS of urban roads by taking users' perceptions and analyzing them through fuzzy set theory. Speed, volume and acceleration noise (defined as the standard deviation of accelerations) are taken as the evaluating criteria. The six categories of LOS A to F of the Highway Capacity Manual are assigned linguistic terms. The users have been divided into two age groups. Their perceptions, recorded through primary surveys, have been used to establish membership functions for different fuzzy sets and to arrive at importance factors assigned to them. Four important corridors of movement in the Metropolitan City of Madras formed the backdrop of the study.