NFPA-EconomicImpactFire-case5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NFPA-CBA Example 5 : Mass timber multi-family apartment building (RES3E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "This Notebook presents an example application of the prototype methodology for cost-benefit analysis. This case study considers a building area of 954 m2 (a ten story multi-family apartment building, based on a design by KPF. The cost-benefits of the following fire protection systems are investigated: (i) sprinkler system, and (ii) structural fire protection by layers of gypsum board over mass timber members.  \n",
    "\n",
    "The reference design has none of the fire protection systems. The cost-benefit of three design alternatives are investigated: (A) presence of sprinkler system only; (B) presence of encapsulation layers only; (C) presence of both fire protection systems.  \n",
    "\n",
    "The benefit evaluation is done considering the event tree diagram visualized below. "
   ]
  },
  {
   "attachments": {
    "Case%205.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Case%205.png](attachment:Case%205.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Input\n",
    "User input in this area.\n",
    "Note: All monetary values in 2022 USD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I.1 Building characteristics\n",
    "High rise apartment building.  \n",
    "Cost evaluation of structure based on cost of comparable structures and the cost of fire protection measures based on (RSMeans, 2022). See background document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "S_area = 909.52 # [m2] Floor area of structure\n",
    "Construction_Cost = 1797.54 # [USD/m2] Construction cost; evaluated through (RSMeans, 2022)\n",
    "Demol_cost = 47.65 # [USD/m2] (RSMeans, 2022) Demolition cost evaluated through (RSMeans, 2022)\n",
    "Disposal_cost = 8.34  # [USD/m2] (RSMeans, 2022) Disposal cost evaluated through (RSMeans, 2022)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I.2 Discount rate and obsolescence rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = 0.03 # [1/year] continuous discount rate; 3% based on (Fischer, 2014)\n",
    "o = 0    # [1/year] obsolescence rate; 0% - building obsolescence neglected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I.3 Cost of fire protection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I.3.a Investment cost fire protection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## sprinkler system\n",
    "c_ins_unitcost= 48.85 # [USD/m2] System installation cost for sprinkler system with wet pipe water sprinkler based on (RSMeans, 2022), assuming light hazard level for the occupancy type.\n",
    "c_ins = c_ins_unitcost*S_area  # [USD] Total system installation cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Encapsulation of timber members with gypsum boards\n",
    "\n",
    "n_comp = np.array([0, 1, 2]) # Number of levels of encapsulation; \n",
    "\n",
    "\n",
    "# Encapsulation wall cost per m2\n",
    "Cost_wall = np.array([0, 52.66, 90.48]) # [USD/m2] Unit cost of encapsulation; evaluated through (RSMeans, 2022)\n",
    "c_comp = Cost_wall * S_area # [USD] Total cost of encapsulation (NB: first scenario is no encapsulation)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I.3.b Maintenance cost fire protection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## sprinkler system\n",
    "m_sp= 0.05 * c_ins # [USD per year] annual maintenance cost; 5% of sprinkler installation cost based on (Hopkin, 2019)- maintenance cost assumed to include replacement of parts for system lifetime extension\n",
    "m_com=0 * c_comp # [USD per year] annual maintenance considered for encapsulation (NB: no maintenance cost for encapsulation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I.4 Benefit of fire protection\n",
    "Benefit evaluation as reduction in cost of fire losses, assessed through flowchart listed in the Introduction.\n",
    "<br> See background document for discussion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I.4.a Fire frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## fire frequency\n",
    "lambda_fi= 0.00151 # [1/year] fire frequency for storage (ignition frequency + unsuccessful immediate suppression by occupants); 0.00156 based on (Manes and Rush, 2019) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I.4.b Fire barrier reliability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## sprinkler reliability\n",
    "psup_sp = 0.95 # [-] probability of succesful fire suppression by sprinklers; 0.92-0.98 based on (Vassart et al., 2012)\n",
    "\n",
    "## encapsulation reliability\n",
    "pf_encap = np.array([0.24,0.02,0]) # probability of building failing with encapsulation of timber members"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I.4.c Fire-induced losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## valuation of risk to life\n",
    "VSL = 5.657*10**6 # [2022 USD] Value of Statistical Life (VSL); 5.657*10**6 based on (ISO 2394:2015) \n",
    "xi_in = (0.047 * VSL) # [2022 USD] valuation of average injury risk; 0.047*VSL based on (Zhuang et al., 2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## [fatalities/fire]\n",
    "Nf_civilian = 7.4/1000 # [fatality/fire] probability of civilian fatalities; 7.4 fatalities per 1000  fires based on (NFPA 2022)\n",
    "Nf_firefighter_onground = 2.4/1e5 # [fatality/fire] probability of firefighter fireground fatality; 2.4/1e5 based on (NFPA, 2022)\n",
    "Nf_firefighter_response = 2.2/1e5 # [fatality/fire] probability of firefighter response fatality; 2.2/1e5 based on (NFPA, 2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## [injuries/fire]\n",
    "Ni_civilian = 3/100 # [injury/fire] civilian injuries; 3 injuries per 100 reported fires based on (NFPA, 2022)\n",
    "Ni_firefighter_onground = 1.62/100 # [injury/fire] firefighter fireground injuries; 1.62 injuries per 100 reported fires based on (NFPA, 2022)\n",
    "Ni_firefighter_response = 0.37/100  # [injury/fire] firefighter response injuries; 3.7 injuries per 1000 reported fires based on (NFPA, 2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## property loss\n",
    "S_loss_0 = S_area # [m2] average damage area in case of no sprinkler suppression; \n",
    "S_loss_s = 4.92 # [m2] average damage area in case of sprinkler suppression; \n",
    "\n",
    "S_loss_comp = 23.8 # [m2] average damage area in case of encapsulation preventing structural failure\n",
    "\n",
    "eps_loss_content = 1.5 # [-] property loss multiplication factor; eps_loss = ((building loss + content loss) / building loss); 1.5 based on (FEMA, 2015)\n",
    "eps_loss_indirect = 1.1  # [-] total property loss multiplication factor accounting for indirect costs; 1.1 based on (Ramachandran and Hall, 2002)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. Design fire risk evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II.1 Calculation of fire scenario parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unit replacement cost\n",
    "Replace_cost = Construction_Cost + Demol_cost + Disposal_cost # [USD/m2] replacement cost as sum of demolition, disposal and reconstruction\n",
    "\n",
    "# Compartment area\n",
    "comp_area = S_loss_comp \n",
    "\n",
    "# survival probability\n",
    "p_encap = 1-pf_encap # probability of building not failing with encapsulation of timber members"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II.2 Scenario consequence evalluation\n",
    "Evaluation of consequences for identified scenarios (event tree- Scenarios I, II and III)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### II.2.a Scenario I \"Suppression by sprinkler\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Fatalities and injuries\n",
    "# Fatalities\n",
    "Nf_sup = 0 + Nf_firefighter_response # [fatality/fire] total fatality rate with sprinkler suppression\n",
    "# Injuries\n",
    "redux_Nin_civ=0.57 # [-] reduction factor for civilian injuries; 57% reduction in civilian injuries, based on (Butry, 2009)\n",
    "Nin_sup = ((1-redux_Nin_civ)*Ni_civilian) + Ni_firefighter_response # [injury/fire] total injury rate with sprinkler suppression\n",
    "\n",
    "### Valuation of fatalities and injuries\n",
    "Df_sup=Nf_sup*VSL # [USD/fire] valuation expected life loss\n",
    "Din_sup = Nin_sup*xi_in # [USD/fire] valuation expected injuries\n",
    "\n",
    "### Material loss\n",
    "xi_d_sup =  S_loss_s * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss given fire\n",
    "\n",
    "### Total loss valuation\n",
    "D_I=Df_sup+Din_sup+xi_d_sup # [USD] expected value fire-induced loss, scenario I \"fire suppressed by sprinkler\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### II.2.b Scenario II \"Not suppressed by sprinklers, but encapsulation prevents building failure\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Fatalities and injuries\n",
    "# Fatalities\n",
    "Nf_sup_encap = Nf_civilian + Nf_firefighter_onground + Nf_firefighter_response # [fatality/fire] total fatality rate without sprinkler suppression \n",
    "# Injuries\n",
    "Nin_sup_encap = Ni_civilian + Ni_firefighter_onground + Nf_firefighter_response # [injury/fire] total injury rate without sprinkler suppression \n",
    "\n",
    "### Valuation of fatalities and injuries\n",
    "Df_sup_encap=Nf_sup_encap*VSL # [USD/fire] valuation expected life loss\n",
    "Din_sup_encap = Nin_sup_encap*xi_in # [USD/fire] valuation expected injuries\n",
    "\n",
    "### Material loss\n",
    "xi_d_sup_encap =  S_loss_comp * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss\n",
    "\n",
    "### Total loss valuation\n",
    "D_II=Df_sup_encap+Din_sup_encap+xi_d_sup_encap # [USD] expected value fire-induced loss, scenario II \"fire not suppressed by sprinklers but ecapsulation prevents building failure\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### II.2.c Scenario III \"Not suppressed, building fails\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Fatalities and injuries\n",
    "# Fatalities\n",
    "Nf_unsup_encap = Nf_civilian + Nf_firefighter_onground + Nf_firefighter_response # [fatality/fire] total fatality rate without sprinkler suppression\n",
    "# Injuries\n",
    "Nin_unsup_encap = Ni_civilian + Ni_firefighter_onground + Nf_firefighter_response # [injury/fire] total injury rate without sprinkler suppression\n",
    "\n",
    "### Valuation of fatalities and injuries\n",
    "Df_unsup_encap=Nf_unsup_encap*VSL # [USD/fire] valuation expected life loss\n",
    "Din_unsup_encap = Nin_unsup_encap*xi_in # [USD/fire] valuation expected injuries\n",
    "\n",
    "### Material loss\n",
    "xi_d_unsup_encap = S_loss_0 * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss\n",
    "\n",
    "### Total loss valuation\n",
    "D_III=Df_unsup_encap+Din_unsup_encap+xi_d_unsup_encap # [USD] expected value fire-induced loss, scenario III \"fire not suppressed\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III. PNV calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total safety system cost *C+A*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.1.a Investment cost, *C<sub>I</sub>*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_I_sp=c_ins # PNV investment cost for sprinkler system\n",
    "C_I_com=c_comp # PNV investment cost for encapsulation  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.1.b PNV maintenance cost, *C<sub>M</sub>*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_M_sp=m_sp/g # PNV of maintenace cost for sprinkler system\n",
    "C_M_com=m_com/g # PNV of maintenace cost for encapsulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.1.c Obsolescence cost, *C<sub>A</sub>*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_A_sp=C_I_sp*o/g # PNV of obsolescence cost sprinkler system\n",
    "C_A_com=C_I_com*o/g # PNV of obsolescence cost encapsulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.1.d Total safety system cost *C+A*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_C_a=C_I_sp+ C_M_sp + C_A_sp # Sprinkler only (Design a)\n",
    "net_C_b = C_I_com + C_M_com + C_A_com # encapsulation only (Design b) - first entry is the reference design of no level of encapsulation\n",
    "net_C_c = net_C_a + net_C_b # Sprinkler and encapsulation both (Design c)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.2 Net Benefit B-D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.2.a Fire induced losses reference design, *B* (no sprinkler system and no encapsulation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expected loss reference design\n",
    "Dref  = ((p_encap[0]) * D_II)+((1-p_encap[0])*D_III)\n",
    "\n",
    "# Present net value of reference design\n",
    "B = Dref * lambda_fi/g # [USD] PNV fire-induced losses reference design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.2.b Fire-induced losses trial designs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Expected losses with design A, B and C\n",
    "# Design a (Sprinkler system only)\n",
    "Da = psup_sp * D_I + (1-psup_sp) *  (p_encap[0] * D_II+(1-p_encap[0])*D_III)\n",
    "\n",
    "# Design b (encapsulation only)\n",
    "Db  =  (p_encap * D_II) + ((1-p_encap) * D_III)\n",
    "\n",
    "# Design c (Fire sprinkler and encapsulation both)\n",
    "Dc  = (psup_sp * D_I) + (1-psup_sp) * (p_encap * D_II + (1-p_encap) * D_III)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PNV of design a, b and c\n",
    "D_a = Da *lambda_fi/g  # [USD] PNV expected risk for alternative design a (sprinkler system only)\n",
    "D_b = Db *lambda_fi/g  # [USD] PNV expected risk for alternative design b (encapsulation only)\n",
    "D_c = Dc *lambda_fi/g  # [USD] PNV expected risk for alternative design c (sprinkler system and encapsulation)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.2.c PNV net benefit, *B-D*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "netB_a = B - D_a # [USD] Net benefit for alternative design a (sprinkler system only)\n",
    "netB_b = B - D_b # [USD] Net benefit for alternative design b (encapsulation only)\n",
    "netB_c = B - D_c # [USD] Net benefit for alternative design c (sprinkler system and encapsulation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV. Cost-Benefit evaluation, Z (USD, Present net value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The PNV for alternative design a (sprinkler system only) is -82,248 [USD]; the BCR is 0.31\n",
      "\n",
      "The PNV for alternative design b (encapsulation only) is, with\n",
      "\t1 layers: -17,900 [USD]; the BCR is 0.63\n",
      "\t2 layers: -49,571 [USD]; the BCR is 0.40\n",
      "\n",
      "The PNV for alternative design c (sprinkler system and encapsulation) is, with\n",
      "\t1 layers: -128,643 [USD]; the BCR is 0.23\n",
      "\t2 layers: -162,905 [USD]; the BCR is 0.19\n"
     ]
    }
   ],
   "source": [
    "## PNV of proposed alternative design\n",
    "Z_a = netB_a - net_C_a # [USD] Present net value for alternative design a (sprinkler system only)\n",
    "Z_b = netB_b - net_C_b # [USD] Present net value for alternative design b (encapsulation only)\n",
    "Z_c = netB_c - net_C_c # [USD] Present net value for alternative design c (sprinkler system and encapsulation)\n",
    "\n",
    "## Benefit cost ratio of proposed alternative design\n",
    "BCR_a=netB_a/net_C_a \n",
    "BCR_b=netB_b[1:]/net_C_b[1:]\n",
    "BCR_c=netB_c[1:]/net_C_c[1:]\n",
    "\n",
    "## Print to screen\n",
    "print(\"\\nThe PNV for alternative design a (sprinkler system only) is {0:,.0f} [USD]; the BCR is {1:.2f}\".format(Z_a,BCR_a))\n",
    "print(\"\\nThe PNV for alternative design b (encapsulation only) is, with\")\n",
    "for i,Z in enumerate(Z_b[1:]):\n",
    "    print(\"\\t{0:,.0f} layers: {1:,.0f} [USD]; the BCR is {2:.2f}\".format(n_comp[i+1],Z,BCR_b[i]))\n",
    "print(\"\\nThe PNV for alternative design c (sprinkler system and encapsulation) is, with\")\n",
    "for i,Z in enumerate(Z_c[1:]):\n",
    "    print(\"\\t{0:,.0f} layers: {1:,.0f} [USD]; the BCR is {2:.2f}\".format(n_comp[i+1],Z,BCR_c[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for cost-benefit of sprinkler and the cost-benefit of encapsulation for failure of both systems\n",
    "plt.rcParams.update({'font.size': 15})\n",
    "fig,ax  = plt.subplots(1,1)\n",
    "\n",
    "#ax.plot([n_comp[0],n_comp[-1]],[Z_a/1000,Z_a/1000],'--',color='r')\n",
    "ax.plot(n_comp[0:],Z_b/1000,linestyle='--',marker='x')\n",
    "ax.plot(n_comp[0:],Z_c/1000,linestyle='--',marker='o')\n",
    "\n",
    "ax.plot([0,2],[0,0],'k')\n",
    "ax.set_xlim([0,2])\n",
    "ax.set_xlabel('Number of layers')\n",
    "ax.set_ylabel('Present net value, Z [$10^3$ USD]')\n",
    "ax.legend(['Encapsulation','Sprinkler + Encapsulation'])\n",
    "fig.set_size_inches(12,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameter study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Study 1: Parameter study with number of encapsulation layers, and indirect cost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Function definition PNV Z, to allow parameter study of encapsulation and sprinkler system values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function combining above model assumption for parameter study\n",
    "def Zvalue(p_encap,eps_loss_indirect):\n",
    "     \n",
    "    ## II. Design fire risk evaluation ##\n",
    "    #####################################    \n",
    "    ## II.2.a Scenario I \"suppression by sprinkler\"\n",
    "    ### Material loss\n",
    "    xi_d_sup =  S_loss_s * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss given fire\n",
    "    ### Total loss valuation\n",
    "    D_I=Df_sup+Din_sup+xi_d_sup # [USD] expected value fire-induced loss, scenario I \"fire suppressed by sprinkler\"    \n",
    "    \n",
    "    ## II.2.b Scenario II \"Not suppressed by sprinklers, no building failure\"\n",
    "    ### Material loss\n",
    "    xi_d_sup_encap =  S_loss_comp * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss\n",
    "    ### Total loss valuation\n",
    "    D_II=Df_sup_encap+Din_sup_encap+xi_d_sup_encap # [USD] expected value fire-induced loss, scenario II \"fire not suppressed by sprinklers but ecapsulation prevents building failure\"\n",
    "    \n",
    "    ## II.2.c Scenario III \"Not suppressed, but failure\"\n",
    "    ### Material loss\n",
    "    xi_d_unsup_encap = S_loss_0 * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss\n",
    "    ### Total loss valuation\n",
    "    D_III=Df_unsup_encap+Din_unsup_encap+xi_d_unsup_encap # [USD] expected value fire-induced loss, scenario III \"fire not suppressed\"    \n",
    "    \n",
    "    ## III. PNV calculation ##\n",
    "    ##########################\n",
    "    ## III.2.a Fire-induced losses reference design, B\n",
    "    # Expected loss reference design\n",
    "    Dref  = ((p_encap[0]) * D_II)+((1-p_encap[0])*D_III)\n",
    "\n",
    "    # Present net value of reference design\n",
    "    B = Dref * lambda_fi/g # [USD] PNV fire-induced losses reference design\n",
    "    \n",
    "    ## III.2.b Fire-induced losses trial designs\n",
    "    ## Expected losses with design A, B and C\n",
    "    \n",
    "    # Design a (Sprinkler system only)\n",
    "    Da = psup_sp * D_I + (1-psup_sp) *  (p_encap[0] * D_II+(1-p_encap[0])*D_III)\n",
    "\n",
    "    # Design b (encapsulation only)\n",
    "    Db  =  (p_encap * D_II) + ((1-p_encap) * D_III)\n",
    "\n",
    "    # Design c (Fire sprinkler and encapsulation both)\n",
    "    Dc  = (psup_sp * D_I) + (1-psup_sp) * (p_encap * D_II + (1-p_encap) * D_III)\n",
    "    \n",
    "    ## PNV evaluation\n",
    "    D_a = Da *lambda_fi/g; D_b = Db *lambda_fi/g; D_c = Dc *lambda_fi/g\n",
    "    \n",
    "    ## III.2.c PNV net benefit, B-D\n",
    "    netB_a = B - D_a; netB_b = B - D_b; netB_c = B - D_c\n",
    "    \n",
    "    ## IV. Cost-Benefit evaluation ##\n",
    "    #################################  \n",
    "    ## PNV of proposed alternative design\n",
    "    Z_a = netB_a - net_C_a # [USD] Present net value for alternative design a (sprinkler system only)\n",
    "    Z_b = netB_b - net_C_b # [USD] Present net value for alternative design b (encapsulation only)\n",
    "    Z_c = netB_c - net_C_c # [USD] Present net value for alternative design c (sprinkler system and encapsulation)\n",
    "\n",
    "    ## Benefit cost ratio of proposed alternative design\n",
    "    BCR_a=netB_a/net_C_a \n",
    "    BCR_b=netB_b[1:]/net_C_b[1:]\n",
    "    BCR_c=netB_c[1:]/net_C_c[1:]\n",
    "    \n",
    "    return Z_a,Z_b,Z_c,BCR_a,BCR_b,BCR_c\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The PNV for alternative design a (sprinkler system only) is -82,248 [USD]; the BCR is 0.31\n",
      "\n",
      "The PNV for alternative design b (encapsulation only) is, with\n",
      "\t1 layers: -17,900 [USD]; the BCR is 0.63\n",
      "\t2 layers: -49,571 [USD]; the BCR is 0.40\n",
      "\n",
      "The PNV for alternative design c (sprinkler system and encapsulation) is, with\n",
      "\t1 layers: -128,643 [USD]; the BCR is 0.23\n",
      "\t2 layers: -162,905 [USD]; the BCR is 0.19\n"
     ]
    }
   ],
   "source": [
    "## Test of Zvalue code\n",
    "\n",
    "# input\n",
    "p_encap_var=p_encap\n",
    "eps_loss_indirect=1.1\n",
    "\n",
    "# calculation\n",
    "Z_a,Z_b,Z_c,BCR_a,BCR_b,BCR_c=Zvalue(p_encap_var,eps_loss_indirect)\n",
    "\n",
    "## Print to screen\n",
    "print(\"\\nThe PNV for alternative design a (sprinkler system only) is {0:,.0f} [USD]; the BCR is {1:.2f}\".format(Z_a,BCR_a))\n",
    "print(\"\\nThe PNV for alternative design b (encapsulation only) is, with\")\n",
    "for i,Z in enumerate(Z_b[1:]):\n",
    "    print(\"\\t{0:,.0f} layers: {1:,.0f} [USD]; the BCR is {2:.2f}\".format(n_comp[i+1],Z,BCR_b[i]))\n",
    "print(\"\\nThe PNV for alternative design c (sprinkler system and encapsulation) is, with\")\n",
    "for i,Z in enumerate(Z_c[1:]):\n",
    "    print(\"\\t{0:,.0f} layers: {1:,.0f} [USD]; the BCR is {2:.2f}\".format(n_comp[i+1],Z,BCR_c[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Indirect cost, encapsulation value, sprinkler value\n",
    "# %matplotlib qt \n",
    "%matplotlib inline\n",
    "p_encap_var = np.array([0.76, 0.98,1])\n",
    "Ind_cost_var = np.array(range(0,105,5)) # [%] indirect cost factor as percentage of total direct cost\n",
    "\n",
    "\n",
    "legends = []\n",
    "\n",
    "eps_loss_indirect_var = 1+Ind_cost_var/100\n",
    "Z_b_var=np.zeros((len(n_comp),len(Ind_cost_var)))\n",
    "\n",
    "Z_c_var=np.zeros((len(n_comp),len(Ind_cost_var)))\n",
    "\n",
    "for j,eps in enumerate(eps_loss_indirect_var):\n",
    "    out = Zvalue(p_encap_var,eps)\n",
    "    Z_b_var[:,j]=out[1]\n",
    "    Z_c_var[:,j] = out[2]\n",
    "\n",
    "fig,ax = plt.subplots(1,1)\n",
    "\n",
    "for i,comp in enumerate(n_comp[0:1]):\n",
    "    ax.plot(Ind_cost_var,Z_c_var[i,:]/1000)\n",
    "    legends.append('Sprinklers only')\n",
    "    \n",
    "for i,comp in enumerate(n_comp[0:2]):\n",
    "    ax.plot(Ind_cost_var,Z_b_var[i+1,:]/1000)\n",
    "    legends.append(str(comp+1)+' encapsulation layers')\n",
    "    \n",
    "for i,comp in enumerate(n_comp[0:2]):\n",
    "    ax.plot(Ind_cost_var,Z_c_var[i+1,:]/1000)\n",
    "    legends.append('Sprinklers + '+str(comp+1)+' encapsulation layers')\n",
    "    \n",
    "    \n",
    "\n",
    "ax.legend(legends,fontsize=12)\n",
    "ax.plot([0,100],[0,0],linestyle='solid',color='k',linewidth=3.0)\n",
    "\n",
    "#ax.grid(which='both',axis='both')\n",
    "\n",
    "ax.set_xlim([0,100])\n",
    "\n",
    "ax.set_xlabel('Indirect cost as a percentage of direct cost(%)')\n",
    "ax.set_ylabel('Present net value, Z [$10^3$ USD]')\n",
    "\n",
    "\n",
    "\n",
    "fig.set_size_inches(9,6)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}