Search arXivSearch

arXiv · 1401.6759

Modeling the behavior of reinforced concrete walls under fire, considering the impact of the span on firewalls

Abstract

Numerical modeling using computers is known to present several advantages compared to experimental testing. The high cost and the amount of time required to prepare and to perform a test were among the main problems on the table when the first tools for modeling structures in fire were developed. The discipline structures-in-fire modeling is still currently the subject of important research efforts around the word, those research efforts led to develop many software. In this paper, our task is oriented to the study of fire behavior and the impact of the span reinforced concrete walls with different sections belonging to a residential building braced by a system composed of porticoes and sails. Regarding the design and mechanical loading (compression forces and moments) exerted on the walls in question, we are based on the results of a study conducted at cold. We use on this subject the software Safir witch obeys to the Eurocode laws, to realize this study. It was found that loading, heating, and sizing play a capital role in the state of failed walls. Our results justify well the use of reinforced concrete walls, acting as a firewall. Their role is to limit the spread of fire from one structure to another structure nearby, since we get fire resistance reaching more than 10 hours depending on the loading considered.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nadia Otmani Benmehidi, Meriem Arar, Imene Chine. 2014-01-27. Modeling the behavior of reinforced concrete walls under fire, considering the impact of the span on firewalls. https://doi.org/10.7321/jscse.v3.n3.91

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Cosm: Collective switched motion for sparse Ising optimization

We introduce Collective Switched Motion (Cosm), a heuristic optimization framework based on switched collective dynamics. Cosm compiles the objective into subobjectives that are activated sequentially, temporally separating competing local influences. The interplay of switching and local interactions among variables gives rise to collective search behavior, while a new correlated perturbation mechanism encourages coordinated cluster motion. Tests on tuned-hardness spin-glass benchmarks suggest more favorable algorithmic scaling than that reported for leading dynamical solvers. Cosm heuristically attains the certified optima of three of the largest Gset instances (G72, G77, G81), exceeding the previously reported heuristic solutions. On the large random-graph instances G61 and G70, a CPU implementation reliably attains the best-known solutions, reaching cuts of 5799 and 9595 with 99%-confidence times-to-target of 15 s and 2.4 s, respectively. On the heterogeneous-degree G64 instance, Cosm establishes a new best-known cut of 8753. Broadly, the results suggest an alternative approach to heuristic design in which local dynamics and orchestration mechanisms are carefully designed so that effective search emerges.

cs.CE

Decomposing Firm-Level Crisis Responses from Incomplete Market Signals: Evidence from China's IT Sector During COVID-19

Exogenous shocks generate heterogeneous behavioral responses across firms, yet event studies typically report only sector-level averages. This paper develops a multi-method approach combining causal identification (difference-in-differences with cluster-robust inference), unsupervised behavioral discovery (K-means trajectory clustering, Gaussian hidden Markov models), and cross-sectional resilience prediction (logistic regression with nested cross-validation) to decompose firm-level response heterogeneity from noisy market signals. We demonstrate the approach on 246 Chinese A-share IT firms (216 with complete data for all analyses) during the COVID-19 shock (January 2020), using 252 non-IT CSI 300 firms as controls. The return decline was market-wide, not IT-specific (DID p = 0.59); the IT-specific effect was elevated volatility (DID beta = 0.043, cluster-robust p < 0.001), with the effect surviving Benjamini-Hochberg correction in 13 of 30 alternative specifications. Unsupervised clustering produced three trajectory groups: fast recovery (36 companies, +29.7%), resilient/moderate (67 companies), and persistent drag (113 companies, -6.9%). Pre-crisis financial fundamentals showed only modest predictive power for resilience (nested CV AUC = 0.635, 95% CI: 0.558-0.711; permutation p = 0.016), consistent with the limited informativeness of publicly available signals for anticipating crisis outcomes. The combination of causal analysis, unsupervised learning, and prediction represents a reproducible framework which can be applied to crises in other market periods.

cs.CE

An Insurance Broker for Every Small Business: The Economics of Exceptional Care at Scale

Small-business owners need expert guidance on their own terms, across schedules, languages, and channels, but low premiums make exceptional, continuous human service uneconomic for much of the market. Combining public evidence, Kinro operational data, and an illustrative five-year service model, we show why traditional brokerage economics leave 35 million U.S. small businesses underserved. An AI-native brokerage can change those economics by performing and coordinating routine work continuously, while licensed professionals govern consequential exceptions and the brokerage remains accountable.

cs.CE