QL-CONST1: an expert system for quality level prediction in concrete structures

Current trends in the fields of artifical intelligence and expert systems are moving towards the exciting possibility of reproducing and simulating human expertise and expert behaviour into a knowledge base, coupled with an appropriate, partially ‘intelligent’, computer code. This paper deals with the quality level prediction in concrete structures using the helpful assistance of an expert system, QL-CONST1, which is able to reason about this specific field of structural engineering. Evidence, hypotheses and factors related to this human knowledge field have been codified into a knowledge base. This knowledge base has been prepared in terms of probabilities of the presence of either hypotheses or evidence and the conditional presence of both. Human experts in the fields of structural engineering and the safety of structures gave their invaluable knowledge and assistance to the construction of the knowledge base. Some illustrative examples for, the validation of the expert system behaviour are included.

Validation of an instrument for injury data collection in strength training

Purpose: To provide for the basis for collecting strength training data using a rigorously validated injury report form. Methods: A group of specialist designed a questionnaire of 45 item grouped into 4 dimensions. Six stages were used to assess face, content, and criterion validity of the weight training injury report form. A 13 members panel assessed the form for face validity, and an expert panel assessed it for content and criterion validity. Panel members were consulted until consensus was reached. A yardstick developed by an expert panel using Intraclass correlation technique was used to assess the reability of the form. Test-retest reliability was assessed with the intraclass correlation coefficient (ICC).The strength training injury report form was developed, and the face, content, and criterion validity successfully assessed. A six step protocol to create a yardstick was also developed to assist in the validation process. Both inter-rater and intra rater reliability results indicated a 98% agreement. Inter-rater reliability agreement of 98% for three injuries. Results: The Cronbach?s alpha of the questionnaire was 0.944 (pmenor que0.01) and the ICC of the entire questionnaire was 0.894 (pmenor que0.01). Conclusion: The questionnaire gathers together enough psychometric properties to be considered a valid and reliable tool for register injury data in strength training, and providing researchers with a basis for future studies in this area. Key Words: data collection; validation; injury prevention; strength training