Prediction Model for Construction Cost and Duration in Jordan

Risk is mitigated in the course of reliable prediction. A probabilistic model is proposed to predict the risk effects on time and cost of construction projects. Project managers and consultants can use the model in estimating project cost and duration based on historic data. Statistical regression models and sample tests are developed using real data of 140 projects. The research objective is to develop a model to predict project cost and duration based on historic data of similar projects. The model result can be used by project managers in the planning phase to validate the schedule critical path time and project budget. Research methodology is steered per the following progression: i) Conduct nonparametric test for project cost and time performance. ii) Develop generic multiple-regression models to predict project cost and duration using historic performance data. iii) The percent prediction error is statistically analyzed; and found to be substantial; thus, iv) Custom multiple regression models are developed for each project type to obtain statistically reliable results. In conclusion, the 95% point estimation of error margin= ±0.035%. Therefore, at a probability of 95%, the proposed model predicts the project cost and duration with a precision of ±0.035% of the mean cost and time

Productivity Improvement of Pre-cast Concrete Installation

The production process of pre-cast concrete installation is analyzed to investigate possible ways for onsite productivity improvement. Although manufactured construction enjoys higher quality and productivity, it is observed that it suffers delays compared to site built construction. Delay causes and respective severity are analyzed for improvement. Firstly, the production process is investigated using the production delay model. Forty cycle data are used in the analysis. The comparative impact and severity are measured for five delay causes, namely: labor, environmental, management, equipment and material on overall system productivity. It is found via the production delay analysis that material, followed by equipment availability then labor were major contributors to system delay. Secondly, statistical analysis on the installation cycle time of three pre-cast component types is carried out, in order to insure whether the delay observed via the first step is attributed to variation of pre-cast pieces. The data used in step one above were not pertinent to product type; therefore, other 90 cycle data are utilized in the statistical analysis, which indicated high variability in cycle time due to product type. Improvement can be achieved through proper scheduling of project equipment and resources. In addition, improvement should target the reduction of installation cycle time variability due to product type

Part I: Navigation

This paper describes the development of an autonomous navigation system suitable for robot navigation in an outdoor, unstructured environment. The navigation system uses a differential global positioning system (DGPS) receiver and digital charge coupled device (CCD) camera for navigation and a laser scanner for obstacle avoidance. The navigation algorithm was developed using a feed forward multi layer neural network. The network was trained using the quasi-Newton back propagation algorithm. Software was developed to simulate the performance and efficiency of the algorithm. The network was able to produce a path with a small MSE compared to the targeted path, which was developed using an experienced driver. The network produced acceptable results when tested under different kinds of roads and obstacles. The significance of the proposed research is that a new level of autonomous robot navigation in unstructured environment had been developed that has large application. 1

A BENCHMARK FOR THE RATE OF CONVERGENCE IN NEURAL NETWORK CLASSIFICATION ALGORITHMS

The purpose of this paper is to demonstrate a new benchmark for comparing the rate of convergence in neural network classification algorithms. The benchmark produces datasets with controllable complexity that can be used by an algorithm. The dataset generator uses the concept of random numbers and linear normalization to generate the data. In a case of a one-layer perceptron, the output datasets are sensitive to weight or bias of the perceptron. A Matlab implemented algorithm analyzed the sample datasets and the benchmark results. The results demonstrate that the convergence time varies based on some selected specifications of the generated dataset. This benchmark and the generated datasets can be used by researches that work on neural network algorithms and are looking for a straightforward and flexible dataset to examine and evaluate the efficiency of neural network classification algorithms