Author
Contributions by role
Author 3
Fortune K. C. Onyelowe
Department of Computer Engineering, Michael Okpara University of Agriculture, Umudike
Summary
Edited Journals
IECE Contributions

Open Access | Research Article | 31 May 2025
Machine Learning Prediction of the Improvement of Black Cotton Soil by Partial Displacement with Quarry Dust and Fly Ash for Sustainable Road Construction
Sustainable Intelligent Infrastructure | Volume 1, Issue 2: 52-66, 2025 | DOI: 10.62762/SII.2025.901022
Abstract
In this research paper, advanced artificial intelligence (AI) techniques have been applied in predicting the mechanical properties of black cotton soil (BCS) treated by the method of partial displacement of the soil. The materials of the displacement operation were fly ash (FA) and quarry dust (QD), which are both solid wastes derived from coal combustion in power plants and quarrying of stones for the production of aggregates. Previous activities show that BCS has never been treated by displacement of the soil sample but by the addition of these cementitious materials as wt % of the dry soil. The advanced AI techniques were the ANN, GP and the EPR, which executed forty data entries collecte... More >

Graphical Abstract
Machine Learning Prediction of the Improvement of Black Cotton Soil by Partial Displacement with Quarry Dust and Fly Ash for Sustainable Road Construction

Open Access | Research Article | 17 April 2025
Development of an Adaptive Neuro-fuzzy Inference System (ANFIS) for Predicting Pavement Deterioration
Sustainable Intelligent Infrastructure | Volume 1, Issue 1: 39-51, 2025 | DOI: 10.62762/SII.2025.494563
Abstract
Pavement maintenance is a critical aspect of transportation and infrastructure management, as it directly impacts traffic flow, vehicle maintenance, safety and accident rate. Effective prediction and prevention of pavement deterioration are essential for optimizing pavement maintenance strategies, reducing cost, and ensuring the lifespan or longevity of transportation. This study presents the development of Adaptive Neuro-Fuzzy inference system (ANFIS) for predicting pavement deterioration. The data used for this analysis is a historical data and field investigation data from the Cross River State pavement Maintenance Agency, Calabar, Nigeria. The ANFIS model was trained using a dataset with... More >

Graphical Abstract
Development of an Adaptive Neuro-fuzzy Inference System (ANFIS) for Predicting Pavement Deterioration

Open Access | Research Article | 24 March 2025 | Cited: 1
Forecasting Earthquake-induced Ground Movement under Seismic Activity Using Response Surface
Sustainable Intelligent Infrastructure | Volume 1, Issue 1: 4-18, 2025 | DOI: 10.62762/SII.2025.846883
Abstract
This study employs Response Surface Methodology (RSM) to model and optimize earthquake-induced ground movements in gravelly geohazard-prone environments. RSM efficiently evaluates the interactions of seismic parameters, including soil type, fault distance, and peak ground acceleration (PGA), reducing computational and experimental efforts. A dataset of 234 entries encompassing 11 seismic and soil stress variables was curated and analyzed, yielding a high-precision predictive model with an R² of 0.9997. The resulting closed-form equation facilitates accurate risk assessment, structural safety optimization, and seismic resilience planning. By identifying critical thresholds and nonlinear rela... More >

Graphical Abstract
Forecasting Earthquake-induced Ground Movement under Seismic Activity Using Response Surface