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Amida, A, Chang, I and Yearwood, D (2019) Designing a practical lab-based assessment: a case study. Journal of Engineering, Design and Technology , 18(03), 567–81.

Amoah, C and Pretorius, L (2019) Evaluation of the impact of risk management on project performance in small construction firms in South Africa. Journal of Engineering, Design and Technology , 18(03), 611–34.

Asiedu, R O and Gyadu-Asiedu, W (2019) Assessing the predictability of construction time overruns using multiple linear regression and Markov chain Monte Carlo. Journal of Engineering, Design and Technology , 18(03), 583–600.

d'Apolito, L and Hong, H (2019) Forklift truck performance simulation and fuel consumption estimation. Journal of Engineering, Design and Technology , 18(03), 689–703.

• Type: Journal Article
• Keywords: Fuel consumption; Forklift; Powertrain; Simulation; Artificial neural network; Modelling;
• ISBN/ISSN: 1726-0531
• URL: https://doi.org/10.1108/JEDT-06-2019-0165
• Abstract:
  Forklift trucks are generally operated with frequent accelerations and stops, reverse and operations of load handling. This way of operation increases the energy losses and consequently the need for reduction of fuel consumption from forklift customers. This study aims to build a model to replicate the performance of forklifts during real operations and estimate fuel consumption without building a real prototype. AVL Cruise has been used to simulate forklift powertrain and hydraulic circuit. The driving cycles used for this study were in accordance with the standard VDI 2198. Artificial neural networks (ANNs), trained by the results of AVL Cruise simulations, have been used to forecast the fuel consumption for a large set of possible driving cycles. The comparison between simulated and experimental data verified that AVL Cruise model was able to simulate the performance of real forklifts, but the results were only valid for the specified driving cycle. The ANNs, trained by the results of AVL Cruise for a certain number of driving cycles, have been found effective to forecast the fuel consumption of a larger number of driving cycles following the prescriptions of the standard VDI 2198. A new method based on ANN, trained by AVL Cruise simulation results, has been introduced to forecast the forklift fuel consumption, reducing the computational time and the cost of experimental tests.

I., B S, L., S R and Ramulu, P J (2019) Surface development by reinforcing nano-composites during friction stir processing – a review. Journal of Engineering, Design and Technology , 18(03), 653–87.

Jiang, Q (2019) Estimation of construction project building cost by back-propagation neural network. Journal of Engineering, Design and Technology , 18(03), 601–9.

Khan, M R and Sonawane, A (2019) Prediction of impact response in construction safety helmet using FEA. Journal of Engineering, Design and Technology , 18(03), 557–66.

Malhotra, M, Sahu, V, Srivastava, A and Misra, A K (2019) Experimental and numerical investigation of the effect of pre-existing utility tunnel on the bearing capacity of shallow footing in sandy soils. Journal of Engineering, Design and Technology , 18(03), 513–29.

Onubi, H O, Yusof, N and Hassan, A S (2019) Adopting green construction practices: health and safety implications. Journal of Engineering, Design and Technology , 18(03), 635–52.

Sahu, V, Attri, R, Gupta, P and Yadav, R (2019) Development of eco friendly brick using water treatment plant sludge and processed tea waste. Journal of Engineering, Design and Technology , 18(03), 727–38.

Santoso, D S and Gallage, P G M P (2019) Critical factors affecting the performance of large construction projects in developing countries. Journal of Engineering, Design and Technology , 18(03), 531–56.

Yap, J B H and Cheah, S Y (2019) Key challenges faced by Chinese contractors in Malaysian construction industry. Journal of Engineering, Design and Technology , 18(03), 705–26.