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This descriptive study aimed to conduct an investigation on the causes and effects of abandonment of construction projects in Ibadan metropolis

Assessment of natural sand being used as fine aggregate for concrete production in Ibadan and its environs was carried out. Ten sources (F1-F10) were selected for the study; four (F5, F6, F7, F8) were river sand sources while six (F1, F2, F3, F4, F9, F10) were burrow pit sand sources. Samples from each source were subjected to sieve analysis, atterberg limit, bulk density, specific gravity, water absorption, sand equivalent, clay lumps and friable particles, amount of materials passing 75μm and organic impurities adopting ASTM standard procedures. Results revealed that sand from river sources met all the criteria for concrete production stated in ASTM standard while sand from burrow pits deviated from limits of the standard in some respects. F10 had water absorption of 2.6% which exceeded maximum 2% specified, F9 was not free from clay lumps and friable particles with a significant value of 6% as against 3% maximum specification. F1, F2, F3, F4, F9 and F10 have more amounts of materials passing the 75µm sieve ranging from 10.8% for F9 to 20.1% for F10 than maximum of 5% in standard specification while F1, F9 and F10 showed an indication of having organic impurities. It is recommended that performance test be conducted on concrete made from burrow pits sand before use for concrete production. The knowledge of this study can be used as a prospecting tool for selecting suitable sand for the production of quality concrete.

A network of the feedforward-type artificial neural networks (ANNs) was used to predict the compressive strength of concrete made from crude oil contaminated soil samples at 3, 7, 14, 28, 56, 84, and 168 days at different degrees of contamination of 2.5%, 5%, 10%, 15%, 20% and 25%. A total of 49 samples were used in the training, testing, and prediction phase of the modeling in the ratio 32 : 11 : 7. The TANH activation function was used and the maximum number of iterations was limited to 20,000 the model used a momentum of 0.6 and a learning rate of 0.031056. Twenty (20) different architectures were considered and the most suitable one was the 2-2-1. Statistical analysis of the output of the network was carried out and the correlation coefficient of the training and testing data is 0.9955712 and 0.980097. The result of the network has shown that the use of neural networks is effective in the prediction of the compressive strength of concrete made from crude oil impacted sand.

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