Tri Joko Wahyu Adi

Microtunneling decision support system (MDS) using Neural-Autoregressive Hidden Markov Model

Research highlights? Our model provides probabilistic prediction of tunnel geology. ? It is designed by combining back propagation Neural Network and autoregressive Hidden Markov Model. ? The prediction accuracy improved because the calculation of posterior probability is not only evaluated from prior states but also updated by observation data given by the neural network. ? The final result of the model - in the form of a probabilistic geology class profile - can be used directly as an input for other decision support systems, such as machine selections and scheduling. Microtunneling is a trenchless technology method used for installing new pipelines. The inherent advantages of this method over open-cut trenching have led to its increasing use. This paper presents a general model for microtunneling decision support system (MDS) that can be used as a basis for developing more effective microtunneling design and construction. The model objectives are to: (1) develop a description of local geology that reflects the uncertainty of the information on which it is based and (2) provide the input data necessary for other decision support systems. MDS is composed of two main modules: (1) geology prediction model (GPM) module which is based on Neural-Autoregressive Hidden Markov Model and (2) excavation method selection module to select appropriate excavation method based on GPM result. In order to validate the proposed model, a microtunneling project: Zhong-he drainage water tunnel in Taiwan, was used as a case study. The result shows that the MDS model achieves these objectives to a satisfactory degrese.

A Dynamic Model for Assessing the Effects of Construction Worker Waste Behavior to Reduce Material Waste

Construction waste has become a pressing issue in many developing countries and has adverse effect on environment, economic and social aspects. The construction industry in Indonesia characterized by the use of the human resources that are more intensive compared to the use of technology. The high use of human resources in the construction industry caused human factor plays an important role an effort to reduce construction waste. As a result, the behavior of construction workers indicated very influentially to the material waste generate from the construction project. The purpose of this research is to determine the relationship between the waste behavior of construction workers toward waste and its effects on material waste reduction. Research variables that affected the waste behavior of construction workers are obtained from the literature review on the previous studies. A dynamic system model used to describe relationships between variables that affected the waste behavior of construction workers and simulate their effects on material waste reduction. Survey respondents verify causal model between variables. The survey models are an interview and a questionnaire survey. It is intended that all variables used and established relationships in the model may represent a real system in the field and to provide an accurate result. Simulation on the basic model is shown the result that the change of waste behavior of construction workers can reduce material waste by 13.30% of the total material waste. The results of the simulation and scenario of the model presented that the organizational management has the highest influence on the material waste reduction due to the changing of workers’ waste behavior, which is 34.58% higher than the base model, and followed by personal factor, organizational culture, and attitudes variables.