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Purpose – The purpose of this paper is to present a case study on the use of Lean Six Sigma principles and tools to study the improvement in design and construction services at a university. The quality of facilities services at universities has been criticized by users calling for improvement. Design/methodology/approach – Quality of facilities services at universities has been criticized by users calling for improvement. The purpose of this paper is to present a case study on using Lean Six Sigma principles and tools to study improving design and construction services at a university. Findings – It was found that non-value-added general improvement review form (GIRF) process steps involving revisions and rework for the design and construction result in time delays, cost increases and quality deficiencies and render cost estimates unreliable; these are unnecessary and should be minimized or eliminated. It was additionally noted that administrative reviews and approvals embedded in GIRF processes slow down work flow, leading to similar problems. Because such steps may be needed for institutional reasons precluding elimination, it was recommended that efforts be directed toward reducing their durations and costs. Overall, the Lean Six Sigma methodology proved to be successful for the intended purpose. Originality/value – Although universities are aware of their facilities services’ quality issues and have been addressing them, no published information is available on how to systematically evaluate and improve such services to increase customer satisfaction. This paper aims at filling this gap.

Artificial neural networks have been effectively used in various civil engineering fields, including construction management and labour productivity. In this study, the performance of the feed forward neural network (FFNN) was compared with radial basis neural network (RBNN) in modelling the productivity of masonry crews. A variety of input factors were incorporated and analysed. Mean absolute percentage error (MAPE) and correlation coefficient (R) were used to evaluate model performance. Research results indicated that the neural computing techniques could be successfully employed in modelling crew productivity. It was also found that successful models could be developed with different combinations of input factors, and several of the models which excluded one or more input factors turned out to be better than the baseline models. Based on the MAPE values obtained for the models, the RBNN technique was found to be better than the FFNN technique, although both slightly overestimated the masons' productivity.

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Tests were conducted to evaluate the feasibility of using ultrasonic testing for stabilization applications. The ultrasonic testing consisted of determining primary-wave (P-wave) velocities of stabilized mixtures. The ultrasonic method involves a simple and fast test procedure that allows for repeated assessment of a sample over time. For the testing program, tests were conducted on a high plasticity clay stabilized with lime, cement, and fly ash and a Type F fly ash stabilized with lime and cement. Compaction characteristics of the mixtures were determined using modified Proctor tests. Unconfined compression tests were used to determine compressive strength and modulus of the mixtures immediately after sample preparation and after 7-day and 28-day curing periods. Ultrasonic tests were conducted on the compaction and compression test samples, and the test results were correlated. Variation of velocity with water content demonstrated a similar trend to the variation of dry density with water content for the soil. The velocity increased with increasing density for both soil and fly ash. For compression characteristics, velocity increased with increasing modulus for both soil and fly ash. The velocity correlated well with the unconfined compressive strength of fly ash samples. However, this trend was not as well defined for the soil. Overall, the test program demonstrated that ultrasonic testing can be used effectively to evaluate stabilized materials. P-wave velocity correlations can be used to verify the quality of field placement of stabilized mixtures and to improve mixture design procedures.