The computational intelligence tool has major contribution to analyse the properties of materials without much experimentation. The B4C particles are used to improve the quality of the strength of materials. With respect to the percentage of these particles used in the micro and nano, composites may fix the mechanical properties. The different combinations of input parameters determine the characteristics of raw materials. The load, content of B4C particles with 0%, 2%, 4%, 6%, 8% and 10% will determine the wear behaviour like CoF, wear rate etc. The properties of materials like stress, strain, % of elongation and impact energy are studied. The temperature based CoF and wear rate is analysed. The temperature may vary between 30°C, 100°C and 200°C. In addition, the CoF and wear rate of materials are predicted with respect to load, weight % of B4C and nano hexagonal boron nitride %. The intelligent tools like Neural Networks (BPNN, RBNN, FL and Decision tree) are applied to analyse these characteristics of micro / nano composites with the inclusion of B4C particles and nano hBN % without physically conducting the experiments in the Lab. The material properties will be classified with respect to the range of input parameters using the computational model.
Construction planning always requires labour productivity estimation. Often, in the case of monolithic construction works, the available catalogues of productivity rates do not provide a reliable assessment. The paper deals with the problem of labour estimation for reinforcement works. An appropriate model of labour prediction problem is being introduced. It includes, between others, staff experience and reinforcement buildability. In the paper it is proposed, that labour requirements can be estimated with aggregated classifiers. The work is a continuation of earlier studies, in which the possibility of using classifier ensembles to predict productivity in monolithic works was investigated.
Work safety control and analysis of accidents during the construction performance are some of the most important issues of the construction management. The paper focuses on the post-accident absence as an element of the occupational safety management. The occurrence of the post-accident absence of workers can be then treated as an indicator of building performance safety. The ability to estimate its length can also facilitate works planning and scheduling in case of the accident. The paper attempts to answer the question whether it is possible and how to use decision trees and their ensembles to predict the severity of the post-accident absence and which classification algorithm is the most promising to solve the prediction problem. The paper clarifies the model of the prediction problem, introduces 5 different decision tress and different aggregation algorithms in order to build the model. Thanks to the use of aggregation methods it is possible to build classifiers that predict precisely and do not require any initial data treatment, which simplifies the prediction process significantly. To identify the most promising classifier or classifier ensemble the prediction accuracy measures of selected classification algorithms were analyzed. The data to build the model was gathered on national (Polish) construction sites and was taken from literature. Models obtained within simulations can be used to build advisory or safety management systems allowing to detect threats while construction works are being planned or carried out.