Bayesian Monte Carlo simulation-driven approach for construction schedule risk inference

As the construction of infrastructures becomes increasingly complex, it has often been challenged by construction delay with enormous losses. The delivery of complex infrastructures provides a rich source of data for new opportunities to understand and address schedule issues. Based on these data, many efforts have been made to identify key construction schedule risks and predict the probability of risk occurrence. Bayesian network is one of the most useful tools for risk inference. However, there are still two obstacles preventing the Bayesian network from being adopted popularly in construction schedule risk management: (1) the development of directed acyclic graph (DAG) and associated conditional probability tables (CPTs); and (2) the lack of observation data to trigger risk inference as evidence at the planning stage. This research aims to develop a novel Bayesian Monte Carlo simulation-driven approach for construction schedule risk inference of infrastructures, where the Bayesian network model can be developed in a more convenient way and be used without observation data required. It first constructs the key risk network with key risks and links through network theory-based analysis. Then the DAG structure of a Bayesian network is developed based on the topological structure of key risk network using deep-first search (DFS) and adapted maximum-weight spanning tree (A-MWST) algorithms. The CPTs are further developed using the leaky-MAX model. Finally, the Bayesian Monte Carlo simulation-driven risk inference method is developed for predicting and quantifying the probability of construction schedule risk occurrence. A real infrastructure project was selected as a case study to verify this developed approach. The results show that the developed approach is more appropriate to deal with risk inference of infrastructures considering its reliability, convenience, and flexibility. This research contributes a new way to construction schedule risk management and provides a novel approach for quantifying and predicting risk occurrence probability

Facile Organocatalyzed Synthesis of Poly(ε-lysine) under Mild Conditions

Functional poly­(amino acid)­s such as poly­(ε-lysine) have many potential high-value applications. However, the effective chemosynthetic strategy for these materials remains a big challenge in polymer chemistry; the key issue is how to design and protect amino groups for the effective ring-opening polymerization (ROP). Our lab succeeded in chemosynthesis of poly­(ε-lysine) via delicate design of a 2,5-dimethylpyrrole protecting group and metal-catalyzed ROP processes, but harsh reaction conditions (e.g., ca. 260 °C) were required. Herein, we developed a superbase <i>t</i>-BuP₄-catalyzed ROP of ε-lactam derivatives, affording high molecular weight poly­(ε-lysine) bearing pendant protected amino groups with high monomer conversion (up to 95%). The organocatalytic polymerization could proceed at low reaction temperature (e.g., 60 °C) compatible with readily removable protecting groups, providing a sustainable and new methodology toward facile preparation of poly­(ε-lysine)

PCA values of six genes.

<p>PCA values of six genes.</p

DA results of 25 samples.

<p>DA results of 25 samples.</p

PCA values of 17 genes.

<p>PCA values of 17 genes.</p

Sequencing-based selection of gene primer sequences and annealing temperatures.

<p>Sequencing-based selection of gene primer sequences and annealing temperatures.</p

HCA results of six genes.

<p>HCA results of six genes.</p

DA results of 17 genes.

<p>DA results of 17 genes.</p

DA results of 6 genes.

<p>DA results of 6 genes.</p

HCA results of 17 genes.

<p>HCA results of 17 genes.</p