Type in a song number or phrase to search for a song. You can search using transliteration into western characters, or using language-specific characters. You can use the * character as a wildcard eg har*heral, or . to represent a single character eg je.us. Click the dropdown to see the many advanced filters available.
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Welcome to Worship Leader. On each page there will be a short help message appearing at the bottom of your screen. To see the full help, touch the message. To turn these messages off, go to the settings page.
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You don't have any sets yet, choose a song and click 'Add Song to Set' to make one
Here you can see a list of any worship sets that you have created. These help you to click forwards and backwards between songs. You can create these by clicking 'Add to Set' when viewing a song.
Here are all the songs in your worship set. You can reorder them by dragging on the reorder icon next to each song, or remove them by clicking the cross icon.
def limit_state_function(self, x): Z, f_y, G, Q = x M_R = Z * f_y M_E = (G + Q) * 2.5 # simplified span/4 return M_R - M_E
def get_target_beta(self): targets_50yr = "CC1": 3.1, "CC2": 3.8, "CC3": 4.3 if self.ref_period == 50: return targets_50yr[self.cc] elif self.ref_period < 50: # Annual beta ~ sqrt(50) relation approx return targets_50yr[self.cc] * np.sqrt(self.ref_period / 50) else: return targets_50yr[self.cc] * np.sqrt(self.ref_period / 50) jcss model code
def compute_form_beta(self): # Transform correlated variables to independent space dists = [self.get_distribution(v, self.vars[v]["nominal"]) for v in self.vars] # Perform FORM (HL-RF algorithm) beta, alpha, x_star = form_hlrf(self.limit_state_function, dists, self.corr) return beta, alpha def limit_state_function(self, x): Z, f_y, G, Q =
| Output | Description | |----------------------------|-------------| | Reliability index ( \beta ) | FORM result | | Failure probability ( P_f ) | ( \Phi(-\beta) ) | | Target ( \beta_target ) | Based on CC & ref. period | | Compliance verdict | PASS / FAIL | | Sensitivity factors ( \alpha ) | Importance of each random variable | | Partial factors (implied) | Equivalent to ( \gamma_m, \gamma_q ) | 5. Pseudo-Code Implementation (Python-like) class JCSSModelCode: def __init__(self, input_json): self.ls = input_json["limit_state"] self.ref_period = input_json["reference_period_years"] self.cc = input_json["consequence_class"] self.vars = input_json["variables"] self.corr = input_json.get("correlations", []) def get_distribution(self, var_name, nominal): """Return scipy distribution based on JCSS Model Code.""" model = self.vars[var_name]["jcss_model"] if model == "steel_yield_strength": mean = nominal * 1.05 cov = 0.08 scale = mean * np.sqrt(np.log(1 + cov**2)) shape = np.sqrt(np.log(1 + cov**2)) return stats.lognorm(s=shape, scale=mean) elif model == "imposed_load_office_50yr_max": # Gumbel parameters from JCSS: mu, beta mu = nominal * 0.6 # example beta = nominal * 0.2 return stats.gumbel_r(loc=mu, scale=beta) # ... others else: return stats.norm(loc=nominal, scale=nominal*0.10) others else: return stats
This is a conceptual development of a feature, intended for integration into structural reliability software (e.g., a digital code-checking or probabilistic design module). The JCSS (Joint Committee on Structural Safety) probabilistic model code provides a unified basis for reliability-based design.
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def limit_state_function(self, x): Z, f_y, G, Q = x M_R = Z * f_y M_E = (G + Q) * 2.5 # simplified span/4 return M_R - M_E
def get_target_beta(self): targets_50yr = "CC1": 3.1, "CC2": 3.8, "CC3": 4.3 if self.ref_period == 50: return targets_50yr[self.cc] elif self.ref_period < 50: # Annual beta ~ sqrt(50) relation approx return targets_50yr[self.cc] * np.sqrt(self.ref_period / 50) else: return targets_50yr[self.cc] * np.sqrt(self.ref_period / 50)
def compute_form_beta(self): # Transform correlated variables to independent space dists = [self.get_distribution(v, self.vars[v]["nominal"]) for v in self.vars] # Perform FORM (HL-RF algorithm) beta, alpha, x_star = form_hlrf(self.limit_state_function, dists, self.corr) return beta, alpha
| Output | Description | |----------------------------|-------------| | Reliability index ( \beta ) | FORM result | | Failure probability ( P_f ) | ( \Phi(-\beta) ) | | Target ( \beta_target ) | Based on CC & ref. period | | Compliance verdict | PASS / FAIL | | Sensitivity factors ( \alpha ) | Importance of each random variable | | Partial factors (implied) | Equivalent to ( \gamma_m, \gamma_q ) | 5. Pseudo-Code Implementation (Python-like) class JCSSModelCode: def __init__(self, input_json): self.ls = input_json["limit_state"] self.ref_period = input_json["reference_period_years"] self.cc = input_json["consequence_class"] self.vars = input_json["variables"] self.corr = input_json.get("correlations", []) def get_distribution(self, var_name, nominal): """Return scipy distribution based on JCSS Model Code.""" model = self.vars[var_name]["jcss_model"] if model == "steel_yield_strength": mean = nominal * 1.05 cov = 0.08 scale = mean * np.sqrt(np.log(1 + cov**2)) shape = np.sqrt(np.log(1 + cov**2)) return stats.lognorm(s=shape, scale=mean) elif model == "imposed_load_office_50yr_max": # Gumbel parameters from JCSS: mu, beta mu = nominal * 0.6 # example beta = nominal * 0.2 return stats.gumbel_r(loc=mu, scale=beta) # ... others else: return stats.norm(loc=nominal, scale=nominal*0.10)
This is a conceptual development of a feature, intended for integration into structural reliability software (e.g., a digital code-checking or probabilistic design module). The JCSS (Joint Committee on Structural Safety) probabilistic model code provides a unified basis for reliability-based design.
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