
Risk Prediction of Delay in the Execution of Public Works Through Fuzzy Logic
Author(s) -
Andrey Valente,
Manoel Henrique Reis Nascimento
Publication year - 2021
Language(s) - English
Resource type - Journals
ISSN - 2411-2933
DOI - 10.31686/ijier.vol9.iss11.3471
Subject(s) - agency (philosophy) , fuzzy logic , authorization , computer science , value (mathematics) , work (physics) , operations research , computer security , mathematics , sociology , artificial intelligence , engineering , social science , machine learning , mechanical engineering
In Manaus, delays in public constructions are not uncommon as their execution deadlines are often extrapolated, even though such deadlines are obtained through preliminary technical studies. The causes that give rise to such delays are varied ranging from the occurrence of rain to the addition of quantities of existing services or additions of new services. In this research, we sought to obtain a model, using fuzzy logic, to predict the risk of delay that some variables may cause in the period of execution of the work, allowing the public administration or the contracted company to adopt measures they consider essential to mitigate this delay. Initially, documentary and bibliographic research were carried out to identify the causes that most contribute to the occurrence of delays in the works. Once these causes were identified, the construction of the fuzzy inference system was started, with six of the most significant causes identified in the research being considered as linguistic variables, namely: the hiring factor, which corresponds to the quotient between the value of the proposal. of the company and the amount budgeted by the administration; the value of the work, which is the value of the contract for the work; the engineering execution drawing, which are the engineering drawings used in the works; the alteration of quantities, which are changes in the quantities of existing services or addition of new services; authorization from public agencies, which corresponds to the permission or support of any public agency or public company for the execution of the work; and the rain. For the simulation of the created fuzzy inference system, real data from four public works were entered, and the answers of this simulation were satisfactory because they are confirmed by the documentation of the respective works. It is concluded that the system proved to be useful, as it was possible to predict the risk of delay in the execution of public works in the city of Manaus, and it can be used by both the public administration and the contractor to mitigate the causes of delays in the execution of public works.