Blasting Overview - Concepedia

Principles

Definition

Blasting is a fundamental computational method within bioinformatics employed for the comparison of biological sequences, such as nucleic acids or proteins, against extensive databases. This heuristic approach efficiently identifies regions of local similarity, serving as a critical tool for homology detection, functional annotation, and evolutionary studies.

Ontological type

Design Parameters

Core Algorithms

Performance Evaluation

Key developments

Key Figures

Mechanistic Detonation Characterization era

Mark D. Zoback [1] is a leading geophysicist whose work in this era involved major institutions such as Stanford University [3] and Columbia University [4]. His key contributions in Mechanistic Detonation Characterization include well bore breakouts and in situ stress, as documented in the 1985 paper Well bore breakouts and in situ stress [5], which advanced mechanistic understanding of initiation and boundary-driven rock response. Craig M. Tarver [2] is a foundational figure in this era, whose contribution is the phenomenological model of shock initiation in heterogeneous explosives [6], shaping theories of initiation thresholds and early growth. This phenomenological framework provided a structured way to relate heterogeneity, initiation, and near-field growth, contributing to safer design benchmarks and credible safety analyses during this era.

Predictive Modeling and Fragmentation era

Manoj Khandelwal [1] is a researcher active in the Predictive Modeling and Fragmentation era, with affiliations including Indian Institute of Technology Bombay [2] and Maharana Pratap University of Agriculture and Technology [3]. His key contribution in this era is the 2009 paper Prediction of blast-induced ground vibration using artificial neural network [4], which demonstrated that artificial neural networks could forecast ground vibration from blast data and thereby complement physics-based fragmentation models to enable multiobjective design exploration. For Manoj Khandelwal [1], this work from the paper Prediction of blast-induced ground vibration using artificial neural network [4] linked input features to ground-vibration outcomes, offering a data-driven tool that supports rapid screening and optimization of blast designs within the era's predictive modeling framework. For Manoj Khandelwal [1], the study's implications in the era were to show that data-driven forecasting could complement continuum damage concepts and meshfree simulations, accelerating design tuning and risk assessment for blast outcomes.

Hybrid Physics-Data Frameworks era

Jian Zhou [1] is associated with Shenzhen University Health Science Center [3] and Chengdu University [4] in the Hybrid Physics-Data Frameworks era. His work evaluates hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration from the paper W3159623433 [7], delivering improved predictive performance and enabling faster, risk-aware blast design. Danial Jahed Armaghani [2] is associated with University of Technology Sydney [5] and University of Malaya [6] in this era. His key contribution is the expert artificial neural network based on particle swarm optimization for blasting-induced flyrock and ground vibration prediction described in W2002536914 [8], illustrating how PSO-tuned neural models advance rapid risk assessment in hybrid frameworks.