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The current Saudi Arabian (SA) procurement system leads to many losses in money and benefits in projects. Also, the use of the traditional procurement system in SA has been identified as one of the causes for poor performance in the delivery of construction and the major risk to the SA government. A questionnaire has been developed and carefully designed based on literature review. The purpose of the survey was to identify the validity of the recent claims that the procurement system in SA is broken and to improve the current SA procurement system. The questionnaire was sent out to 1,396 participants including included 867 engineers, 256 consultants, 93 contractors, 35 owners and 132 architects and 13 academics.
All participants have been registered and licensed professionals at the SA Council for professional engineers, who work in both private and public sectors. The participants are interested in the SA procurement and contracts system with experience ranging from one to more than twenty-five years with the majority of twenty-five years of experience in common construction sectors such as; residential and commercial buildings, healthcare buildings, industrial building and heavy civil construction.
Most of the participants from both private and public sectors agreed with the survey questions subject matter regarding: zone price proposals, contractors' evaluation, risks, planning, projects' scope, owners concern and weekly risks reports (WRR). The survey results showed that the procurement system is the major risk to projects, affects construction projects negatively and is in need of improvement.
Based on the survey and literature review, a model, called Saudi government performance procurement model (SGPPM), has been developed in which the most expert contractor is chosen through four phases: submittals& education, vendors selection, illustration and execution. The resulting model is easy to implement by SA government and does not require special skills or backgrounds.
The purpose of this project is to create a useful tool for musicians that utilizes the harmonic content of their playing to recommend new, relevant chords to play. This is done by training various Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) on the lead sheets of 100 different jazz standards. A total of 200 unique datasets were produced and tested, resulting in the prediction of nearly 51 million chords. A note-prediction accuracy of 82.1% and a chord-prediction accuracy of 34.5% were achieved across all datasets. Methods of data representation that were rooted in valid music theory frameworks were found to increase the efficacy of harmonic prediction by up to 6%. Optimal LSTM input sizes were also determined for each method of data representation.