TITLE:
An Evolving Fuzzy Neural Network for Characterizing and Predicting Asymmetric-Information Risk Causes in Road Maintenance Projects: A Kenyan Case Study
AUTHORS:
Joseph Maina Kaberenge, Cyrus Babu, Isaac Fundi
KEYWORDS:
Asymmetric Information, Construction Risk, Evolving Fuzzy Neural Network, Takagi-Sugeno-Kang, Road Maintenance, Kenya
JOURNAL NAME:
Open Journal of Civil Engineering,
Vol.16 No.3,
September
3,
2026
ABSTRACT: Asymmetric information, meaning the uneven spread of project-relevant knowledge among clients, contractors and consultants, is a persistent but weakly quantified source of construction risk, and it matters a great deal for publicly funded road maintenance in developing economies. This paper characterises the asymmetric-information risk causes of a Kenyan urban road maintenance project, the Periodic Maintenance and Spot Improvement of Kinoo Shopping Centre Loop (Tender No. KURA/DEV/HQ/311/2024-2025), and develops an evolving fuzzy neural network (EFuNN) that tracks them while remaining interpretable. Ten risk indicators covering the contractor, client, consultant and external domains were read off the tender document, the conditions of contract and its data sheet, the bills of quantities, the Form 7 schedule of construction material basic prices and the environmental management plan. Each indicator was given an explicit scoring rule tied to a named clause and fuzzified into three Gaussian linguistic granules. Because the weekly progress records for the case lot were not released, the indicator stream was reconstructed rather than observed: the clause-derived scores set the level of each trajectory, contract provisions set its shape over the project lifecycle, and bounded random variation was added to give 208 weekly samples across the case lot and seven comparable KURA maintenance lots. A five-layer evolving Takagi-Sugeno-Kang network with novelty-based rule creation, utility pruning and recursive-least-squares adaptation was trained prequentially on this stream. It grew a compact seven-rule base and recovered the specified composite risk index with a root-mean-square error of 0.031, a mean absolute error of 0.024 and a coefficient of determination of 0.83. These figures measure how closely the network reproduces a reference index that this paper defines in Equations (2) and (3); they are not a test against realised claims, delays or cost growth, and they should be read as evidence of online structural identification rather than empirical validation. The fuzzy categorisation points to market volatility, time pressure and liquidated-damages exposure as the most adverse causes, each traceable to a specific clause of the contract. The study contributes a documented and auditable route from contract clauses to a continuous, time-varying risk signal, together with evidence that an evolving neuro-fuzzy predictor can follow such a signal while staying small enough to read.